CONVOLUTIONAL NEURAL NETWORKS FOR DETECTION OF MALFORMATIONS OF CORTICAL DEVELOPMENT¶
Import packages and functions¶
In [1]:
import matplotlib as mpl
%matplotlib inline
from PIL import Image
import numpy as np
import pandas as pd
import os
from skimage.color import gray2rgb
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.axes_grid1 import ImageGrid
from sklearn.utils import shuffle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import activations
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, concatenate, Dense, Dropout, Activation, Flatten, GaussianNoise, BatchNormalization, GlobalAveragePooling2D, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.keras.models import Model
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from tensorflow.keras.models import model_from_json
from tensorflow.keras import backend as K
from tensorflow.keras.utils import to_categorical
from tf_keras_vis.gradcam import Gradcam
from tf_keras_vis.saliency import Saliency
from tf_keras_vis.utils import normalize
from sklearn.metrics import classification_report
In [2]:
# Define image size
mpl.rcParams['figure.figsize'] = (20,24)
FIRST PART: DATA INGESTION¶
Data description¶
After having trained and validated our CNNs, we will test them with the test data:
-338 normal MRI images from 17 control patients
-242 MRI images of diffuse malformations of cortical development from 14 patients
Import original images¶
In [3]:
# Unzip files
!unzip ~/data/Controltest.zip -d ~/data/
!unzip ~/data/CMtest.zip -d ~/data/
# Remove the zipped files
!rm ~/data/Controltest.zip
!rm ~/data/CMtest.zip
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/home/ubuntu/data/CMtest/7.6_T2 TSE Axial_31.jpg inflating: /home/ubuntu/data/CMtest/8.1_AX T1 RECON_92.jpg inflating: /home/ubuntu/data/CMtest/8.10_COR T1 RECON_92.jpg inflating: /home/ubuntu/data/CMtest/8.11_SAG MPRAGE_47.jpg inflating: /home/ubuntu/data/CMtest/8.12_SAG MPRAGE_54.jpg inflating: /home/ubuntu/data/CMtest/8.13_SAG MPRAGE_59.jpg inflating: /home/ubuntu/data/CMtest/8.2_AX T1 RECON_98.jpg inflating: /home/ubuntu/data/CMtest/8.3_AX TSE T2_31.jpg inflating: /home/ubuntu/data/CMtest/8.4_AX TSE T2_36.jpg inflating: /home/ubuntu/data/CMtest/8.5_AX TSE T2_42.jpg inflating: /home/ubuntu/data/CMtest/8.6_AX TSE T2_43.jpg inflating: /home/ubuntu/data/CMtest/8.7_AX TSE T2_52.jpg inflating: /home/ubuntu/data/CMtest/8.8_COR T1 RECON_75.jpg inflating: /home/ubuntu/data/CMtest/8.9_COR T1 RECON_85.jpg inflating: /home/ubuntu/data/CMtest/9.1_AX T2_29.jpg inflating: /home/ubuntu/data/CMtest/9.10_AX T2 FLAIR_31.jpg inflating: /home/ubuntu/data/CMtest/9.11_AX MPRAGE RECON_145.jpg inflating: 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Paths to original and processed images¶
In [4]:
# Path to the folder with the original images
pathtoimagesControltest = './data/Controltest/'
pathtoimagesCMtest = './data/CMtest/'
# Create directories to save the processed images
! mkdir ~/data/processedControltest
! mkdir ~/data/processedCMtest
# Path to the folder with the processed images
pathtoprocessedimagesControltest = './data/processedControltest/'
pathtoprocessedimagesCMtest = './data/processedCMtest/'
Read in and preprocess Controltest images¶
In [5]:
# Define the image size
image_size = (512, 512)
# Read in the training images
Controltest_dir = pathtoimagesControltest
Controltest_files = os.listdir(Controltest_dir)
# For each image
for f in Controltest_files:
# Open the image
img = Image.open(Controltest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedControltest/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
Read in and preprocess CMtest images¶
In [6]:
# Define the image size
image_size = (512, 512)
# Read in the training images
CMtest_dir = pathtoimagesCMtest
CMtest_files = os.listdir(CMtest_dir)
# For each image
for f in CMtest_files:
# Open the image
img = Image.open(CMtest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array with no page number and save it into the preprocessed folder
img_arr = np.array(img)
img_arr[462:512, 0:100, :] = np.mean(img_arr[452:462, 0:100, :])
processed_img = Image.fromarray(img_arr, 'RGB')
processed_img_name = './data/processedCMtest/'+'processed'+str(np.random.randint(low=1, high=1e8))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e4, high=1e6))+ \
str(np.random.randint(low=1e4, high=1e6))+str(np.random.randint(low=1e5, high=1e8))+ \
str(np.random.randint(low=1e2, high=1e7))+str(np.random.randint(low=1e3, high=1e5))+ \
str(np.random.randint(low=1e2, high=1e8))+'.jpg'
processed_img.save(processed_img_name)
SECOND PART: IMPORTATION OF FINAL DATA¶
Path to the final images¶
In [7]:
# Create directories for the final images
!mkdir ~/data/FinalControltest
!mkdir ~/data/FinalCMtest
In [8]:
# Copy all processed images to the final folders
!cp ./data/processedControltest/* ./data/FinalControltest/
!cp ./data/processedCMtest/* ./data/FinalCMtest/
In [9]:
## Path to final images
pathtofinalControltest = './data/FinalControltest/'
pathtofinalCMtest = './data/FinalCMtest/'
Import images and labels for the test set¶
In [10]:
## CONTROLS
# Define the image size
image_size = (512, 512)
# Read in the test images for controls
Controltest_images = []
Controltest_dir = pathtofinalControltest
Controltest_files = os.listdir(Controltest_dir)
# For each image
for f in Controltest_files:
# Open the image
img = Image.open(Controltest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
Controltest_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
Controltest_X = np.array(Controltest_images)
# Create an array of labels (0 for controls)
Controltest_y = np.array([[0]*Controltest_X.shape[0]]).T
## DIFFUSE CM
# Read in the test images for CM
CMtest_images = []
CMtest_dir = pathtofinalCMtest
CMtest_files = os.listdir(CMtest_dir)
# For each image
for f in CMtest_files:
# Open the image
img = Image.open(CMtest_dir + f)
# Resize the image so that it has a size 512x512
img = img.resize(image_size)
# Transform into a numpy array
img_arr = np.array(img)
# Add the image to the array of images
CMtest_images.append(img_arr)
# After having transformed all images, transform the list into a numpy array
CMtest_X = np.array(CMtest_images)
# Create an array of labels (1 for CM)
CMtest_y = np.array([[1]*CMtest_X.shape[0]]).T
## MERGE CONTROLS AND DIFFUSE CM
# Train merge files
test_X = np.concatenate([Controltest_X, CMtest_X])
test_y = np.vstack((Controltest_y, CMtest_y))
# GPU expects values to be 32-bit floats
test_X = test_X.astype(np.float32)
# Rescale the pixel values to be between 0 and 1
test_X /= 255.
In [11]:
# Shuffle in unison the test_X and the test_y array (123 is just a random number for reproducibility)
shuffled_test_X, shuffled_test_y = shuffle(test_X, test_y, random_state=123)
# Transform outcome to one-hot encoding
shuffled_test_y = to_categorical(shuffled_test_y)
In [12]:
# Make sure that the dimensions are as expected
shuffled_test_X.shape
Out[12]:
(580, 512, 512, 3)
In [13]:
# Example of an image to make sure they were converted right
plt.imshow(shuffled_test_X[0])
plt.grid(b=None)
plt.xticks([])
plt.yticks([])
plt.show()
In [14]:
# Make sure that the dimensions are as expected
shuffled_test_y.shape
Out[14]:
(580, 2)
In [15]:
# Make sure that the label is correct for the image
shuffled_test_y[0]
Out[15]:
array([1., 0.], dtype=float32)
THIRD PART: EVALUATE THE NEURAL NETWORK¶
Load the model which performed best in the validation set¶
In [16]:
# load model
json_file = open('InceptionResNetV2.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("InceptionResNetV2.h5")
Compile the model¶
In [17]:
# Compile model
model.compile(optimizer = Adam(lr = 0.0001), loss = 'categorical_crossentropy', metrics = ['accuracy'])
Evaluate the model¶
In [18]:
# Generate predictions in the form of probabilities for the test set
testInceptionResNetV2 = model.predict(shuffled_test_X, batch_size = 32)
# Generate the confusion matrix in the test set
y_true = np.argmax(shuffled_test_y, axis=1)
y_predInceptionResNetV2 = np.argmax(testInceptionResNetV2, axis=1)
# Confusion matrix
pd.DataFrame(confusion_matrix(y_true, y_predInceptionResNetV2), index=['True: Normal', 'True: Diffuse CM'], columns=['Prediction: Normal', 'Prediction: Diffuse CM']).T
Out[18]:
| True: Normal | True: Diffuse CM | |
|---|---|---|
| Prediction: Normal | 299 | 70 |
| Prediction: Diffuse CM | 39 | 172 |
In [19]:
# Calculate accuracy in the test set
accuracy_InceptionResNetV2 = accuracy_score(y_true=y_true, y_pred=y_predInceptionResNetV2)
print('The accuracy in the test set is {:.4f}.'.format(accuracy_InceptionResNetV2))
The accuracy in the test set is 0.8121.
In [20]:
# Calculate AUC in the test set
auc_validInceptionResNetV2 = roc_auc_score(shuffled_test_y, model.predict(shuffled_test_X))
print('The AUC in the test set is {:.4f}.'.format(auc_validInceptionResNetV2))
The AUC in the test set is 0.8869.
In [21]:
# Classification report
print(classification_report(y_true, y_predInceptionResNetV2, target_names=['Normal MRI', 'Diffuse CM']))
precision recall f1-score support
Normal MRI 0.81 0.88 0.85 338
Diffuse CM 0.82 0.71 0.76 242
accuracy 0.81 580
macro avg 0.81 0.80 0.80 580
weighted avg 0.81 0.81 0.81 580
Model visualization¶
In [22]:
# Visualize the structure and layers of the model
model.layers
Out[22]:
[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7fce2fcc9eb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fcd6513d2b0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fcd7780>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fcd7b70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fcd7d30>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fcd7f60>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fc8f160>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fc8f320>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fc8f550>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fc8f710>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7fce2fc8f940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fc8fac8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fc8fcf8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fc8feb8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fc99128>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fc99358>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fc99550>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7fce2fc99780>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fc99908>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fc99b38>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fc99cf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fc99f28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2fca2198>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fca23c8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2fca25c0>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2fca2908>, 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<tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8facf8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8faf28>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d906198>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d906390>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d9066d8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d906a20>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d906c50>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d906d30>, <tensorflow.python.keras.layers.pooling.MaxPooling2D at 0x7fce2d906e10>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d906f98>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d90f1d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d90f3c8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d90f588>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d90f7b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d90f9e8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d90fbe0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d90fe10>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d918080>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d9182b0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d9184a8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d9187f0>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d918a20>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d918b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d918be0>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d918e48>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d918f60>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d9260b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d9262e8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d9264a8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d9266d8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d926908>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d926b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d926d30>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d926f60>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8b01d0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8b03c8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8b0710>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8b0940>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d8b0a20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8b0b00>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d8b0d68>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8b0e80>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8b0f98>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8bc208>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8bc3c8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8bc5f8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8bc828>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8bca20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8bcc50>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8bce80>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8c70f0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8c72e8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8c7630>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8c7860>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d8c7940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8c7a20>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d8c7c88>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8c7da0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8c7eb8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8d0128>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8d02e8>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8d0518>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8d0748>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8d0940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8d0b70>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8d0da0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8d0fd0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8dc208>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8dc550>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8dc780>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d8dc860>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8dc940>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d8dcba8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8dccc0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8dcdd8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8e7048>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8e7208>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8e7438>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8e7668>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8e7860>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8e7a90>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8e7cc0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8e7ef0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d873128>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d873470>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8736a0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d873780>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d873860>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d873ac8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d873be0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d873cf8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d873f28>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d87d128>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d87d358>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d87d588>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d87d780>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d87d9b0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d87dbe0>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d87de10>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d886048>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d886390>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8865c0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d8866a0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d886780>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d8869e8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d886b00>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d886c18>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d886e48>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d895048>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d895278>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8954a8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8956a0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8958d0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d895b00>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d895d30>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d895f28>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d89f2b0>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d89f4e0>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d89f5c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d89f6a0>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d89f908>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d89fa20>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d89fb38>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d89fd68>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d89ff28>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8a9198>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8a93c8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8a95c0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8a97f0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8a9a20>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8a9c50>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8a9e48>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8361d0>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d836400>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d8364e0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8365c0>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d836828>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d836940>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d836a58>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d836c88>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d836e48>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d8420b8>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8422e8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d8424e0>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d842710>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d842940>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d842b70>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d842d68>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d84c0f0>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d84c320>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d84c400>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d84c4e0>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d84c748>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d84c860>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d84c978>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d84cba8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d84cd68>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d84cf98>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d855208>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d855400>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d855630>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d855860>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d855a90>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d855c88>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d855fd0>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d860240>, <tensorflow.python.keras.layers.merge.Concatenate at 0x7fce2d860320>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d860400>, <tensorflow.python.keras.layers.core.Lambda at 0x7fce2d860668>, <tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fce2d860780>, <tensorflow.python.keras.layers.normalization.BatchNormalization at 0x7fce2d8609e8>, <tensorflow.python.keras.layers.core.Activation at 0x7fce2d860ba8>, <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7fce2d860dd8>, <tensorflow.python.keras.layers.core.Dense at 0x7fce2d860ef0>, <tensorflow.python.keras.layers.core.Dropout at 0x7fce2d7ec128>, <tensorflow.python.keras.layers.core.Dense at 0x7fce2d7ec208>, <tensorflow.python.keras.layers.core.Dropout at 0x7fce2d7ec438>, <tensorflow.python.keras.layers.core.Dense at 0x7fce2d7ec518>, <tensorflow.python.keras.layers.core.Dense at 0x7fce2d7ec748>]
In [23]:
# Visualize the structure and layers of the model
print(model.summary())
Model: "model_123"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d_129 (Conv2D) (None, 255, 255, 32) 864 input_4[0][0]
__________________________________________________________________________________________________
batch_normalization_98 (BatchNo (None, 255, 255, 32) 96 conv2d_129[0][0]
__________________________________________________________________________________________________
activation_98 (Activation) (None, 255, 255, 32) 0 batch_normalization_98[0][0]
__________________________________________________________________________________________________
conv2d_130 (Conv2D) (None, 253, 253, 32) 9216 activation_98[0][0]
__________________________________________________________________________________________________
batch_normalization_99 (BatchNo (None, 253, 253, 32) 96 conv2d_130[0][0]
__________________________________________________________________________________________________
activation_99 (Activation) (None, 253, 253, 32) 0 batch_normalization_99[0][0]
__________________________________________________________________________________________________
conv2d_131 (Conv2D) (None, 253, 253, 64) 18432 activation_99[0][0]
__________________________________________________________________________________________________
batch_normalization_100 (BatchN (None, 253, 253, 64) 192 conv2d_131[0][0]
__________________________________________________________________________________________________
activation_100 (Activation) (None, 253, 253, 64) 0 batch_normalization_100[0][0]
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 126, 126, 64) 0 activation_100[0][0]
__________________________________________________________________________________________________
conv2d_132 (Conv2D) (None, 126, 126, 80) 5120 max_pooling2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_101 (BatchN (None, 126, 126, 80) 240 conv2d_132[0][0]
__________________________________________________________________________________________________
activation_101 (Activation) (None, 126, 126, 80) 0 batch_normalization_101[0][0]
__________________________________________________________________________________________________
conv2d_133 (Conv2D) (None, 124, 124, 192 138240 activation_101[0][0]
__________________________________________________________________________________________________
batch_normalization_102 (BatchN (None, 124, 124, 192 576 conv2d_133[0][0]
__________________________________________________________________________________________________
activation_102 (Activation) (None, 124, 124, 192 0 batch_normalization_102[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 61, 61, 192) 0 activation_102[0][0]
__________________________________________________________________________________________________
conv2d_137 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
batch_normalization_106 (BatchN (None, 61, 61, 64) 192 conv2d_137[0][0]
__________________________________________________________________________________________________
activation_106 (Activation) (None, 61, 61, 64) 0 batch_normalization_106[0][0]
__________________________________________________________________________________________________
conv2d_135 (Conv2D) (None, 61, 61, 48) 9216 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_138 (Conv2D) (None, 61, 61, 96) 55296 activation_106[0][0]
__________________________________________________________________________________________________
batch_normalization_104 (BatchN (None, 61, 61, 48) 144 conv2d_135[0][0]
__________________________________________________________________________________________________
batch_normalization_107 (BatchN (None, 61, 61, 96) 288 conv2d_138[0][0]
__________________________________________________________________________________________________
activation_104 (Activation) (None, 61, 61, 48) 0 batch_normalization_104[0][0]
__________________________________________________________________________________________________
activation_107 (Activation) (None, 61, 61, 96) 0 batch_normalization_107[0][0]
__________________________________________________________________________________________________
average_pooling2d_9 (AveragePoo (None, 61, 61, 192) 0 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_134 (Conv2D) (None, 61, 61, 96) 18432 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_136 (Conv2D) (None, 61, 61, 64) 76800 activation_104[0][0]
__________________________________________________________________________________________________
conv2d_139 (Conv2D) (None, 61, 61, 96) 82944 activation_107[0][0]
__________________________________________________________________________________________________
conv2d_140 (Conv2D) (None, 61, 61, 64) 12288 average_pooling2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_103 (BatchN (None, 61, 61, 96) 288 conv2d_134[0][0]
__________________________________________________________________________________________________
batch_normalization_105 (BatchN (None, 61, 61, 64) 192 conv2d_136[0][0]
__________________________________________________________________________________________________
batch_normalization_108 (BatchN (None, 61, 61, 96) 288 conv2d_139[0][0]
__________________________________________________________________________________________________
batch_normalization_109 (BatchN (None, 61, 61, 64) 192 conv2d_140[0][0]
__________________________________________________________________________________________________
activation_103 (Activation) (None, 61, 61, 96) 0 batch_normalization_103[0][0]
__________________________________________________________________________________________________
activation_105 (Activation) (None, 61, 61, 64) 0 batch_normalization_105[0][0]
__________________________________________________________________________________________________
activation_108 (Activation) (None, 61, 61, 96) 0 batch_normalization_108[0][0]
__________________________________________________________________________________________________
activation_109 (Activation) (None, 61, 61, 64) 0 batch_normalization_109[0][0]
__________________________________________________________________________________________________
mixed_5b (Concatenate) (None, 61, 61, 320) 0 activation_103[0][0]
activation_105[0][0]
activation_108[0][0]
activation_109[0][0]
__________________________________________________________________________________________________
conv2d_144 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
batch_normalization_113 (BatchN (None, 61, 61, 32) 96 conv2d_144[0][0]
__________________________________________________________________________________________________
activation_113 (Activation) (None, 61, 61, 32) 0 batch_normalization_113[0][0]
__________________________________________________________________________________________________
conv2d_142 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
conv2d_145 (Conv2D) (None, 61, 61, 48) 13824 activation_113[0][0]
__________________________________________________________________________________________________
batch_normalization_111 (BatchN (None, 61, 61, 32) 96 conv2d_142[0][0]
__________________________________________________________________________________________________
batch_normalization_114 (BatchN (None, 61, 61, 48) 144 conv2d_145[0][0]
__________________________________________________________________________________________________
activation_111 (Activation) (None, 61, 61, 32) 0 batch_normalization_111[0][0]
__________________________________________________________________________________________________
activation_114 (Activation) (None, 61, 61, 48) 0 batch_normalization_114[0][0]
__________________________________________________________________________________________________
conv2d_141 (Conv2D) (None, 61, 61, 32) 10240 mixed_5b[0][0]
__________________________________________________________________________________________________
conv2d_143 (Conv2D) (None, 61, 61, 32) 9216 activation_111[0][0]
__________________________________________________________________________________________________
conv2d_146 (Conv2D) (None, 61, 61, 64) 27648 activation_114[0][0]
__________________________________________________________________________________________________
batch_normalization_110 (BatchN (None, 61, 61, 32) 96 conv2d_141[0][0]
__________________________________________________________________________________________________
batch_normalization_112 (BatchN (None, 61, 61, 32) 96 conv2d_143[0][0]
__________________________________________________________________________________________________
batch_normalization_115 (BatchN (None, 61, 61, 64) 192 conv2d_146[0][0]
__________________________________________________________________________________________________
activation_110 (Activation) (None, 61, 61, 32) 0 batch_normalization_110[0][0]
__________________________________________________________________________________________________
activation_112 (Activation) (None, 61, 61, 32) 0 batch_normalization_112[0][0]
__________________________________________________________________________________________________
activation_115 (Activation) (None, 61, 61, 64) 0 batch_normalization_115[0][0]
__________________________________________________________________________________________________
block35_1_mixed (Concatenate) (None, 61, 61, 128) 0 activation_110[0][0]
activation_112[0][0]
activation_115[0][0]
__________________________________________________________________________________________________
block35_1_conv (Conv2D) (None, 61, 61, 320) 41280 block35_1_mixed[0][0]
__________________________________________________________________________________________________
block35_1 (Lambda) (None, 61, 61, 320) 0 mixed_5b[0][0]
block35_1_conv[0][0]
__________________________________________________________________________________________________
block35_1_ac (Activation) (None, 61, 61, 320) 0 block35_1[0][0]
__________________________________________________________________________________________________
conv2d_150 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_119 (BatchN (None, 61, 61, 32) 96 conv2d_150[0][0]
__________________________________________________________________________________________________
activation_119 (Activation) (None, 61, 61, 32) 0 batch_normalization_119[0][0]
__________________________________________________________________________________________________
conv2d_148 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_151 (Conv2D) (None, 61, 61, 48) 13824 activation_119[0][0]
__________________________________________________________________________________________________
batch_normalization_117 (BatchN (None, 61, 61, 32) 96 conv2d_148[0][0]
__________________________________________________________________________________________________
batch_normalization_120 (BatchN (None, 61, 61, 48) 144 conv2d_151[0][0]
__________________________________________________________________________________________________
activation_117 (Activation) (None, 61, 61, 32) 0 batch_normalization_117[0][0]
__________________________________________________________________________________________________
activation_120 (Activation) (None, 61, 61, 48) 0 batch_normalization_120[0][0]
__________________________________________________________________________________________________
conv2d_147 (Conv2D) (None, 61, 61, 32) 10240 block35_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_149 (Conv2D) (None, 61, 61, 32) 9216 activation_117[0][0]
__________________________________________________________________________________________________
conv2d_152 (Conv2D) (None, 61, 61, 64) 27648 activation_120[0][0]
__________________________________________________________________________________________________
batch_normalization_116 (BatchN (None, 61, 61, 32) 96 conv2d_147[0][0]
__________________________________________________________________________________________________
batch_normalization_118 (BatchN (None, 61, 61, 32) 96 conv2d_149[0][0]
__________________________________________________________________________________________________
batch_normalization_121 (BatchN (None, 61, 61, 64) 192 conv2d_152[0][0]
__________________________________________________________________________________________________
activation_116 (Activation) (None, 61, 61, 32) 0 batch_normalization_116[0][0]
__________________________________________________________________________________________________
activation_118 (Activation) (None, 61, 61, 32) 0 batch_normalization_118[0][0]
__________________________________________________________________________________________________
activation_121 (Activation) (None, 61, 61, 64) 0 batch_normalization_121[0][0]
__________________________________________________________________________________________________
block35_2_mixed (Concatenate) (None, 61, 61, 128) 0 activation_116[0][0]
activation_118[0][0]
activation_121[0][0]
__________________________________________________________________________________________________
block35_2_conv (Conv2D) (None, 61, 61, 320) 41280 block35_2_mixed[0][0]
__________________________________________________________________________________________________
block35_2 (Lambda) (None, 61, 61, 320) 0 block35_1_ac[0][0]
block35_2_conv[0][0]
__________________________________________________________________________________________________
block35_2_ac (Activation) (None, 61, 61, 320) 0 block35_2[0][0]
__________________________________________________________________________________________________
conv2d_156 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_125 (BatchN (None, 61, 61, 32) 96 conv2d_156[0][0]
__________________________________________________________________________________________________
activation_125 (Activation) (None, 61, 61, 32) 0 batch_normalization_125[0][0]
__________________________________________________________________________________________________
conv2d_154 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_157 (Conv2D) (None, 61, 61, 48) 13824 activation_125[0][0]
__________________________________________________________________________________________________
batch_normalization_123 (BatchN (None, 61, 61, 32) 96 conv2d_154[0][0]
__________________________________________________________________________________________________
batch_normalization_126 (BatchN (None, 61, 61, 48) 144 conv2d_157[0][0]
__________________________________________________________________________________________________
activation_123 (Activation) (None, 61, 61, 32) 0 batch_normalization_123[0][0]
__________________________________________________________________________________________________
activation_126 (Activation) (None, 61, 61, 48) 0 batch_normalization_126[0][0]
__________________________________________________________________________________________________
conv2d_153 (Conv2D) (None, 61, 61, 32) 10240 block35_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_155 (Conv2D) (None, 61, 61, 32) 9216 activation_123[0][0]
__________________________________________________________________________________________________
conv2d_158 (Conv2D) (None, 61, 61, 64) 27648 activation_126[0][0]
__________________________________________________________________________________________________
batch_normalization_122 (BatchN (None, 61, 61, 32) 96 conv2d_153[0][0]
__________________________________________________________________________________________________
batch_normalization_124 (BatchN (None, 61, 61, 32) 96 conv2d_155[0][0]
__________________________________________________________________________________________________
batch_normalization_127 (BatchN (None, 61, 61, 64) 192 conv2d_158[0][0]
__________________________________________________________________________________________________
activation_122 (Activation) (None, 61, 61, 32) 0 batch_normalization_122[0][0]
__________________________________________________________________________________________________
activation_124 (Activation) (None, 61, 61, 32) 0 batch_normalization_124[0][0]
__________________________________________________________________________________________________
activation_127 (Activation) (None, 61, 61, 64) 0 batch_normalization_127[0][0]
__________________________________________________________________________________________________
block35_3_mixed (Concatenate) (None, 61, 61, 128) 0 activation_122[0][0]
activation_124[0][0]
activation_127[0][0]
__________________________________________________________________________________________________
block35_3_conv (Conv2D) (None, 61, 61, 320) 41280 block35_3_mixed[0][0]
__________________________________________________________________________________________________
block35_3 (Lambda) (None, 61, 61, 320) 0 block35_2_ac[0][0]
block35_3_conv[0][0]
__________________________________________________________________________________________________
block35_3_ac (Activation) (None, 61, 61, 320) 0 block35_3[0][0]
__________________________________________________________________________________________________
conv2d_162 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_131 (BatchN (None, 61, 61, 32) 96 conv2d_162[0][0]
__________________________________________________________________________________________________
activation_131 (Activation) (None, 61, 61, 32) 0 batch_normalization_131[0][0]
__________________________________________________________________________________________________
conv2d_160 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_163 (Conv2D) (None, 61, 61, 48) 13824 activation_131[0][0]
__________________________________________________________________________________________________
batch_normalization_129 (BatchN (None, 61, 61, 32) 96 conv2d_160[0][0]
__________________________________________________________________________________________________
batch_normalization_132 (BatchN (None, 61, 61, 48) 144 conv2d_163[0][0]
__________________________________________________________________________________________________
activation_129 (Activation) (None, 61, 61, 32) 0 batch_normalization_129[0][0]
__________________________________________________________________________________________________
activation_132 (Activation) (None, 61, 61, 48) 0 batch_normalization_132[0][0]
__________________________________________________________________________________________________
conv2d_159 (Conv2D) (None, 61, 61, 32) 10240 block35_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_161 (Conv2D) (None, 61, 61, 32) 9216 activation_129[0][0]
__________________________________________________________________________________________________
conv2d_164 (Conv2D) (None, 61, 61, 64) 27648 activation_132[0][0]
__________________________________________________________________________________________________
batch_normalization_128 (BatchN (None, 61, 61, 32) 96 conv2d_159[0][0]
__________________________________________________________________________________________________
batch_normalization_130 (BatchN (None, 61, 61, 32) 96 conv2d_161[0][0]
__________________________________________________________________________________________________
batch_normalization_133 (BatchN (None, 61, 61, 64) 192 conv2d_164[0][0]
__________________________________________________________________________________________________
activation_128 (Activation) (None, 61, 61, 32) 0 batch_normalization_128[0][0]
__________________________________________________________________________________________________
activation_130 (Activation) (None, 61, 61, 32) 0 batch_normalization_130[0][0]
__________________________________________________________________________________________________
activation_133 (Activation) (None, 61, 61, 64) 0 batch_normalization_133[0][0]
__________________________________________________________________________________________________
block35_4_mixed (Concatenate) (None, 61, 61, 128) 0 activation_128[0][0]
activation_130[0][0]
activation_133[0][0]
__________________________________________________________________________________________________
block35_4_conv (Conv2D) (None, 61, 61, 320) 41280 block35_4_mixed[0][0]
__________________________________________________________________________________________________
block35_4 (Lambda) (None, 61, 61, 320) 0 block35_3_ac[0][0]
block35_4_conv[0][0]
__________________________________________________________________________________________________
block35_4_ac (Activation) (None, 61, 61, 320) 0 block35_4[0][0]
__________________________________________________________________________________________________
conv2d_168 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_137 (BatchN (None, 61, 61, 32) 96 conv2d_168[0][0]
__________________________________________________________________________________________________
activation_137 (Activation) (None, 61, 61, 32) 0 batch_normalization_137[0][0]
__________________________________________________________________________________________________
conv2d_166 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_169 (Conv2D) (None, 61, 61, 48) 13824 activation_137[0][0]
__________________________________________________________________________________________________
batch_normalization_135 (BatchN (None, 61, 61, 32) 96 conv2d_166[0][0]
__________________________________________________________________________________________________
batch_normalization_138 (BatchN (None, 61, 61, 48) 144 conv2d_169[0][0]
__________________________________________________________________________________________________
activation_135 (Activation) (None, 61, 61, 32) 0 batch_normalization_135[0][0]
__________________________________________________________________________________________________
activation_138 (Activation) (None, 61, 61, 48) 0 batch_normalization_138[0][0]
__________________________________________________________________________________________________
conv2d_165 (Conv2D) (None, 61, 61, 32) 10240 block35_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_167 (Conv2D) (None, 61, 61, 32) 9216 activation_135[0][0]
__________________________________________________________________________________________________
conv2d_170 (Conv2D) (None, 61, 61, 64) 27648 activation_138[0][0]
__________________________________________________________________________________________________
batch_normalization_134 (BatchN (None, 61, 61, 32) 96 conv2d_165[0][0]
__________________________________________________________________________________________________
batch_normalization_136 (BatchN (None, 61, 61, 32) 96 conv2d_167[0][0]
__________________________________________________________________________________________________
batch_normalization_139 (BatchN (None, 61, 61, 64) 192 conv2d_170[0][0]
__________________________________________________________________________________________________
activation_134 (Activation) (None, 61, 61, 32) 0 batch_normalization_134[0][0]
__________________________________________________________________________________________________
activation_136 (Activation) (None, 61, 61, 32) 0 batch_normalization_136[0][0]
__________________________________________________________________________________________________
activation_139 (Activation) (None, 61, 61, 64) 0 batch_normalization_139[0][0]
__________________________________________________________________________________________________
block35_5_mixed (Concatenate) (None, 61, 61, 128) 0 activation_134[0][0]
activation_136[0][0]
activation_139[0][0]
__________________________________________________________________________________________________
block35_5_conv (Conv2D) (None, 61, 61, 320) 41280 block35_5_mixed[0][0]
__________________________________________________________________________________________________
block35_5 (Lambda) (None, 61, 61, 320) 0 block35_4_ac[0][0]
block35_5_conv[0][0]
__________________________________________________________________________________________________
block35_5_ac (Activation) (None, 61, 61, 320) 0 block35_5[0][0]
__________________________________________________________________________________________________
conv2d_174 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_143 (BatchN (None, 61, 61, 32) 96 conv2d_174[0][0]
__________________________________________________________________________________________________
activation_143 (Activation) (None, 61, 61, 32) 0 batch_normalization_143[0][0]
__________________________________________________________________________________________________
conv2d_172 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_175 (Conv2D) (None, 61, 61, 48) 13824 activation_143[0][0]
__________________________________________________________________________________________________
batch_normalization_141 (BatchN (None, 61, 61, 32) 96 conv2d_172[0][0]
__________________________________________________________________________________________________
batch_normalization_144 (BatchN (None, 61, 61, 48) 144 conv2d_175[0][0]
__________________________________________________________________________________________________
activation_141 (Activation) (None, 61, 61, 32) 0 batch_normalization_141[0][0]
__________________________________________________________________________________________________
activation_144 (Activation) (None, 61, 61, 48) 0 batch_normalization_144[0][0]
__________________________________________________________________________________________________
conv2d_171 (Conv2D) (None, 61, 61, 32) 10240 block35_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_173 (Conv2D) (None, 61, 61, 32) 9216 activation_141[0][0]
__________________________________________________________________________________________________
conv2d_176 (Conv2D) (None, 61, 61, 64) 27648 activation_144[0][0]
__________________________________________________________________________________________________
batch_normalization_140 (BatchN (None, 61, 61, 32) 96 conv2d_171[0][0]
__________________________________________________________________________________________________
batch_normalization_142 (BatchN (None, 61, 61, 32) 96 conv2d_173[0][0]
__________________________________________________________________________________________________
batch_normalization_145 (BatchN (None, 61, 61, 64) 192 conv2d_176[0][0]
__________________________________________________________________________________________________
activation_140 (Activation) (None, 61, 61, 32) 0 batch_normalization_140[0][0]
__________________________________________________________________________________________________
activation_142 (Activation) (None, 61, 61, 32) 0 batch_normalization_142[0][0]
__________________________________________________________________________________________________
activation_145 (Activation) (None, 61, 61, 64) 0 batch_normalization_145[0][0]
__________________________________________________________________________________________________
block35_6_mixed (Concatenate) (None, 61, 61, 128) 0 activation_140[0][0]
activation_142[0][0]
activation_145[0][0]
__________________________________________________________________________________________________
block35_6_conv (Conv2D) (None, 61, 61, 320) 41280 block35_6_mixed[0][0]
__________________________________________________________________________________________________
block35_6 (Lambda) (None, 61, 61, 320) 0 block35_5_ac[0][0]
block35_6_conv[0][0]
__________________________________________________________________________________________________
block35_6_ac (Activation) (None, 61, 61, 320) 0 block35_6[0][0]
__________________________________________________________________________________________________
conv2d_180 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_149 (BatchN (None, 61, 61, 32) 96 conv2d_180[0][0]
__________________________________________________________________________________________________
activation_149 (Activation) (None, 61, 61, 32) 0 batch_normalization_149[0][0]
__________________________________________________________________________________________________
conv2d_178 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_181 (Conv2D) (None, 61, 61, 48) 13824 activation_149[0][0]
__________________________________________________________________________________________________
batch_normalization_147 (BatchN (None, 61, 61, 32) 96 conv2d_178[0][0]
__________________________________________________________________________________________________
batch_normalization_150 (BatchN (None, 61, 61, 48) 144 conv2d_181[0][0]
__________________________________________________________________________________________________
activation_147 (Activation) (None, 61, 61, 32) 0 batch_normalization_147[0][0]
__________________________________________________________________________________________________
activation_150 (Activation) (None, 61, 61, 48) 0 batch_normalization_150[0][0]
__________________________________________________________________________________________________
conv2d_177 (Conv2D) (None, 61, 61, 32) 10240 block35_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_179 (Conv2D) (None, 61, 61, 32) 9216 activation_147[0][0]
__________________________________________________________________________________________________
conv2d_182 (Conv2D) (None, 61, 61, 64) 27648 activation_150[0][0]
__________________________________________________________________________________________________
batch_normalization_146 (BatchN (None, 61, 61, 32) 96 conv2d_177[0][0]
__________________________________________________________________________________________________
batch_normalization_148 (BatchN (None, 61, 61, 32) 96 conv2d_179[0][0]
__________________________________________________________________________________________________
batch_normalization_151 (BatchN (None, 61, 61, 64) 192 conv2d_182[0][0]
__________________________________________________________________________________________________
activation_146 (Activation) (None, 61, 61, 32) 0 batch_normalization_146[0][0]
__________________________________________________________________________________________________
activation_148 (Activation) (None, 61, 61, 32) 0 batch_normalization_148[0][0]
__________________________________________________________________________________________________
activation_151 (Activation) (None, 61, 61, 64) 0 batch_normalization_151[0][0]
__________________________________________________________________________________________________
block35_7_mixed (Concatenate) (None, 61, 61, 128) 0 activation_146[0][0]
activation_148[0][0]
activation_151[0][0]
__________________________________________________________________________________________________
block35_7_conv (Conv2D) (None, 61, 61, 320) 41280 block35_7_mixed[0][0]
__________________________________________________________________________________________________
block35_7 (Lambda) (None, 61, 61, 320) 0 block35_6_ac[0][0]
block35_7_conv[0][0]
__________________________________________________________________________________________________
block35_7_ac (Activation) (None, 61, 61, 320) 0 block35_7[0][0]
__________________________________________________________________________________________________
conv2d_186 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_155 (BatchN (None, 61, 61, 32) 96 conv2d_186[0][0]
__________________________________________________________________________________________________
activation_155 (Activation) (None, 61, 61, 32) 0 batch_normalization_155[0][0]
__________________________________________________________________________________________________
conv2d_184 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_187 (Conv2D) (None, 61, 61, 48) 13824 activation_155[0][0]
__________________________________________________________________________________________________
batch_normalization_153 (BatchN (None, 61, 61, 32) 96 conv2d_184[0][0]
__________________________________________________________________________________________________
batch_normalization_156 (BatchN (None, 61, 61, 48) 144 conv2d_187[0][0]
__________________________________________________________________________________________________
activation_153 (Activation) (None, 61, 61, 32) 0 batch_normalization_153[0][0]
__________________________________________________________________________________________________
activation_156 (Activation) (None, 61, 61, 48) 0 batch_normalization_156[0][0]
__________________________________________________________________________________________________
conv2d_183 (Conv2D) (None, 61, 61, 32) 10240 block35_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_185 (Conv2D) (None, 61, 61, 32) 9216 activation_153[0][0]
__________________________________________________________________________________________________
conv2d_188 (Conv2D) (None, 61, 61, 64) 27648 activation_156[0][0]
__________________________________________________________________________________________________
batch_normalization_152 (BatchN (None, 61, 61, 32) 96 conv2d_183[0][0]
__________________________________________________________________________________________________
batch_normalization_154 (BatchN (None, 61, 61, 32) 96 conv2d_185[0][0]
__________________________________________________________________________________________________
batch_normalization_157 (BatchN (None, 61, 61, 64) 192 conv2d_188[0][0]
__________________________________________________________________________________________________
activation_152 (Activation) (None, 61, 61, 32) 0 batch_normalization_152[0][0]
__________________________________________________________________________________________________
activation_154 (Activation) (None, 61, 61, 32) 0 batch_normalization_154[0][0]
__________________________________________________________________________________________________
activation_157 (Activation) (None, 61, 61, 64) 0 batch_normalization_157[0][0]
__________________________________________________________________________________________________
block35_8_mixed (Concatenate) (None, 61, 61, 128) 0 activation_152[0][0]
activation_154[0][0]
activation_157[0][0]
__________________________________________________________________________________________________
block35_8_conv (Conv2D) (None, 61, 61, 320) 41280 block35_8_mixed[0][0]
__________________________________________________________________________________________________
block35_8 (Lambda) (None, 61, 61, 320) 0 block35_7_ac[0][0]
block35_8_conv[0][0]
__________________________________________________________________________________________________
block35_8_ac (Activation) (None, 61, 61, 320) 0 block35_8[0][0]
__________________________________________________________________________________________________
conv2d_192 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_161 (BatchN (None, 61, 61, 32) 96 conv2d_192[0][0]
__________________________________________________________________________________________________
activation_161 (Activation) (None, 61, 61, 32) 0 batch_normalization_161[0][0]
__________________________________________________________________________________________________
conv2d_190 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_193 (Conv2D) (None, 61, 61, 48) 13824 activation_161[0][0]
__________________________________________________________________________________________________
batch_normalization_159 (BatchN (None, 61, 61, 32) 96 conv2d_190[0][0]
__________________________________________________________________________________________________
batch_normalization_162 (BatchN (None, 61, 61, 48) 144 conv2d_193[0][0]
__________________________________________________________________________________________________
activation_159 (Activation) (None, 61, 61, 32) 0 batch_normalization_159[0][0]
__________________________________________________________________________________________________
activation_162 (Activation) (None, 61, 61, 48) 0 batch_normalization_162[0][0]
__________________________________________________________________________________________________
conv2d_189 (Conv2D) (None, 61, 61, 32) 10240 block35_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_191 (Conv2D) (None, 61, 61, 32) 9216 activation_159[0][0]
__________________________________________________________________________________________________
conv2d_194 (Conv2D) (None, 61, 61, 64) 27648 activation_162[0][0]
__________________________________________________________________________________________________
batch_normalization_158 (BatchN (None, 61, 61, 32) 96 conv2d_189[0][0]
__________________________________________________________________________________________________
batch_normalization_160 (BatchN (None, 61, 61, 32) 96 conv2d_191[0][0]
__________________________________________________________________________________________________
batch_normalization_163 (BatchN (None, 61, 61, 64) 192 conv2d_194[0][0]
__________________________________________________________________________________________________
activation_158 (Activation) (None, 61, 61, 32) 0 batch_normalization_158[0][0]
__________________________________________________________________________________________________
activation_160 (Activation) (None, 61, 61, 32) 0 batch_normalization_160[0][0]
__________________________________________________________________________________________________
activation_163 (Activation) (None, 61, 61, 64) 0 batch_normalization_163[0][0]
__________________________________________________________________________________________________
block35_9_mixed (Concatenate) (None, 61, 61, 128) 0 activation_158[0][0]
activation_160[0][0]
activation_163[0][0]
__________________________________________________________________________________________________
block35_9_conv (Conv2D) (None, 61, 61, 320) 41280 block35_9_mixed[0][0]
__________________________________________________________________________________________________
block35_9 (Lambda) (None, 61, 61, 320) 0 block35_8_ac[0][0]
block35_9_conv[0][0]
__________________________________________________________________________________________________
block35_9_ac (Activation) (None, 61, 61, 320) 0 block35_9[0][0]
__________________________________________________________________________________________________
conv2d_198 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_167 (BatchN (None, 61, 61, 32) 96 conv2d_198[0][0]
__________________________________________________________________________________________________
activation_167 (Activation) (None, 61, 61, 32) 0 batch_normalization_167[0][0]
__________________________________________________________________________________________________
conv2d_196 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_199 (Conv2D) (None, 61, 61, 48) 13824 activation_167[0][0]
__________________________________________________________________________________________________
batch_normalization_165 (BatchN (None, 61, 61, 32) 96 conv2d_196[0][0]
__________________________________________________________________________________________________
batch_normalization_168 (BatchN (None, 61, 61, 48) 144 conv2d_199[0][0]
__________________________________________________________________________________________________
activation_165 (Activation) (None, 61, 61, 32) 0 batch_normalization_165[0][0]
__________________________________________________________________________________________________
activation_168 (Activation) (None, 61, 61, 48) 0 batch_normalization_168[0][0]
__________________________________________________________________________________________________
conv2d_195 (Conv2D) (None, 61, 61, 32) 10240 block35_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_197 (Conv2D) (None, 61, 61, 32) 9216 activation_165[0][0]
__________________________________________________________________________________________________
conv2d_200 (Conv2D) (None, 61, 61, 64) 27648 activation_168[0][0]
__________________________________________________________________________________________________
batch_normalization_164 (BatchN (None, 61, 61, 32) 96 conv2d_195[0][0]
__________________________________________________________________________________________________
batch_normalization_166 (BatchN (None, 61, 61, 32) 96 conv2d_197[0][0]
__________________________________________________________________________________________________
batch_normalization_169 (BatchN (None, 61, 61, 64) 192 conv2d_200[0][0]
__________________________________________________________________________________________________
activation_164 (Activation) (None, 61, 61, 32) 0 batch_normalization_164[0][0]
__________________________________________________________________________________________________
activation_166 (Activation) (None, 61, 61, 32) 0 batch_normalization_166[0][0]
__________________________________________________________________________________________________
activation_169 (Activation) (None, 61, 61, 64) 0 batch_normalization_169[0][0]
__________________________________________________________________________________________________
block35_10_mixed (Concatenate) (None, 61, 61, 128) 0 activation_164[0][0]
activation_166[0][0]
activation_169[0][0]
__________________________________________________________________________________________________
block35_10_conv (Conv2D) (None, 61, 61, 320) 41280 block35_10_mixed[0][0]
__________________________________________________________________________________________________
block35_10 (Lambda) (None, 61, 61, 320) 0 block35_9_ac[0][0]
block35_10_conv[0][0]
__________________________________________________________________________________________________
block35_10_ac (Activation) (None, 61, 61, 320) 0 block35_10[0][0]
__________________________________________________________________________________________________
conv2d_202 (Conv2D) (None, 61, 61, 256) 81920 block35_10_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_171 (BatchN (None, 61, 61, 256) 768 conv2d_202[0][0]
__________________________________________________________________________________________________
activation_171 (Activation) (None, 61, 61, 256) 0 batch_normalization_171[0][0]
__________________________________________________________________________________________________
conv2d_203 (Conv2D) (None, 61, 61, 256) 589824 activation_171[0][0]
__________________________________________________________________________________________________
batch_normalization_172 (BatchN (None, 61, 61, 256) 768 conv2d_203[0][0]
__________________________________________________________________________________________________
activation_172 (Activation) (None, 61, 61, 256) 0 batch_normalization_172[0][0]
__________________________________________________________________________________________________
conv2d_201 (Conv2D) (None, 30, 30, 384) 1105920 block35_10_ac[0][0]
__________________________________________________________________________________________________
conv2d_204 (Conv2D) (None, 30, 30, 384) 884736 activation_172[0][0]
__________________________________________________________________________________________________
batch_normalization_170 (BatchN (None, 30, 30, 384) 1152 conv2d_201[0][0]
__________________________________________________________________________________________________
batch_normalization_173 (BatchN (None, 30, 30, 384) 1152 conv2d_204[0][0]
__________________________________________________________________________________________________
activation_170 (Activation) (None, 30, 30, 384) 0 batch_normalization_170[0][0]
__________________________________________________________________________________________________
activation_173 (Activation) (None, 30, 30, 384) 0 batch_normalization_173[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 30, 30, 320) 0 block35_10_ac[0][0]
__________________________________________________________________________________________________
mixed_6a (Concatenate) (None, 30, 30, 1088) 0 activation_170[0][0]
activation_173[0][0]
max_pooling2d_16[0][0]
__________________________________________________________________________________________________
conv2d_206 (Conv2D) (None, 30, 30, 128) 139264 mixed_6a[0][0]
__________________________________________________________________________________________________
batch_normalization_175 (BatchN (None, 30, 30, 128) 384 conv2d_206[0][0]
__________________________________________________________________________________________________
activation_175 (Activation) (None, 30, 30, 128) 0 batch_normalization_175[0][0]
__________________________________________________________________________________________________
conv2d_207 (Conv2D) (None, 30, 30, 160) 143360 activation_175[0][0]
__________________________________________________________________________________________________
batch_normalization_176 (BatchN (None, 30, 30, 160) 480 conv2d_207[0][0]
__________________________________________________________________________________________________
activation_176 (Activation) (None, 30, 30, 160) 0 batch_normalization_176[0][0]
__________________________________________________________________________________________________
conv2d_205 (Conv2D) (None, 30, 30, 192) 208896 mixed_6a[0][0]
__________________________________________________________________________________________________
conv2d_208 (Conv2D) (None, 30, 30, 192) 215040 activation_176[0][0]
__________________________________________________________________________________________________
batch_normalization_174 (BatchN (None, 30, 30, 192) 576 conv2d_205[0][0]
__________________________________________________________________________________________________
batch_normalization_177 (BatchN (None, 30, 30, 192) 576 conv2d_208[0][0]
__________________________________________________________________________________________________
activation_174 (Activation) (None, 30, 30, 192) 0 batch_normalization_174[0][0]
__________________________________________________________________________________________________
activation_177 (Activation) (None, 30, 30, 192) 0 batch_normalization_177[0][0]
__________________________________________________________________________________________________
block17_1_mixed (Concatenate) (None, 30, 30, 384) 0 activation_174[0][0]
activation_177[0][0]
__________________________________________________________________________________________________
block17_1_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_1_mixed[0][0]
__________________________________________________________________________________________________
block17_1 (Lambda) (None, 30, 30, 1088) 0 mixed_6a[0][0]
block17_1_conv[0][0]
__________________________________________________________________________________________________
block17_1_ac (Activation) (None, 30, 30, 1088) 0 block17_1[0][0]
__________________________________________________________________________________________________
conv2d_210 (Conv2D) (None, 30, 30, 128) 139264 block17_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_179 (BatchN (None, 30, 30, 128) 384 conv2d_210[0][0]
__________________________________________________________________________________________________
activation_179 (Activation) (None, 30, 30, 128) 0 batch_normalization_179[0][0]
__________________________________________________________________________________________________
conv2d_211 (Conv2D) (None, 30, 30, 160) 143360 activation_179[0][0]
__________________________________________________________________________________________________
batch_normalization_180 (BatchN (None, 30, 30, 160) 480 conv2d_211[0][0]
__________________________________________________________________________________________________
activation_180 (Activation) (None, 30, 30, 160) 0 batch_normalization_180[0][0]
__________________________________________________________________________________________________
conv2d_209 (Conv2D) (None, 30, 30, 192) 208896 block17_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_212 (Conv2D) (None, 30, 30, 192) 215040 activation_180[0][0]
__________________________________________________________________________________________________
batch_normalization_178 (BatchN (None, 30, 30, 192) 576 conv2d_209[0][0]
__________________________________________________________________________________________________
batch_normalization_181 (BatchN (None, 30, 30, 192) 576 conv2d_212[0][0]
__________________________________________________________________________________________________
activation_178 (Activation) (None, 30, 30, 192) 0 batch_normalization_178[0][0]
__________________________________________________________________________________________________
activation_181 (Activation) (None, 30, 30, 192) 0 batch_normalization_181[0][0]
__________________________________________________________________________________________________
block17_2_mixed (Concatenate) (None, 30, 30, 384) 0 activation_178[0][0]
activation_181[0][0]
__________________________________________________________________________________________________
block17_2_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_2_mixed[0][0]
__________________________________________________________________________________________________
block17_2 (Lambda) (None, 30, 30, 1088) 0 block17_1_ac[0][0]
block17_2_conv[0][0]
__________________________________________________________________________________________________
block17_2_ac (Activation) (None, 30, 30, 1088) 0 block17_2[0][0]
__________________________________________________________________________________________________
conv2d_214 (Conv2D) (None, 30, 30, 128) 139264 block17_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_183 (BatchN (None, 30, 30, 128) 384 conv2d_214[0][0]
__________________________________________________________________________________________________
activation_183 (Activation) (None, 30, 30, 128) 0 batch_normalization_183[0][0]
__________________________________________________________________________________________________
conv2d_215 (Conv2D) (None, 30, 30, 160) 143360 activation_183[0][0]
__________________________________________________________________________________________________
batch_normalization_184 (BatchN (None, 30, 30, 160) 480 conv2d_215[0][0]
__________________________________________________________________________________________________
activation_184 (Activation) (None, 30, 30, 160) 0 batch_normalization_184[0][0]
__________________________________________________________________________________________________
conv2d_213 (Conv2D) (None, 30, 30, 192) 208896 block17_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_216 (Conv2D) (None, 30, 30, 192) 215040 activation_184[0][0]
__________________________________________________________________________________________________
batch_normalization_182 (BatchN (None, 30, 30, 192) 576 conv2d_213[0][0]
__________________________________________________________________________________________________
batch_normalization_185 (BatchN (None, 30, 30, 192) 576 conv2d_216[0][0]
__________________________________________________________________________________________________
activation_182 (Activation) (None, 30, 30, 192) 0 batch_normalization_182[0][0]
__________________________________________________________________________________________________
activation_185 (Activation) (None, 30, 30, 192) 0 batch_normalization_185[0][0]
__________________________________________________________________________________________________
block17_3_mixed (Concatenate) (None, 30, 30, 384) 0 activation_182[0][0]
activation_185[0][0]
__________________________________________________________________________________________________
block17_3_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_3_mixed[0][0]
__________________________________________________________________________________________________
block17_3 (Lambda) (None, 30, 30, 1088) 0 block17_2_ac[0][0]
block17_3_conv[0][0]
__________________________________________________________________________________________________
block17_3_ac (Activation) (None, 30, 30, 1088) 0 block17_3[0][0]
__________________________________________________________________________________________________
conv2d_218 (Conv2D) (None, 30, 30, 128) 139264 block17_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_187 (BatchN (None, 30, 30, 128) 384 conv2d_218[0][0]
__________________________________________________________________________________________________
activation_187 (Activation) (None, 30, 30, 128) 0 batch_normalization_187[0][0]
__________________________________________________________________________________________________
conv2d_219 (Conv2D) (None, 30, 30, 160) 143360 activation_187[0][0]
__________________________________________________________________________________________________
batch_normalization_188 (BatchN (None, 30, 30, 160) 480 conv2d_219[0][0]
__________________________________________________________________________________________________
activation_188 (Activation) (None, 30, 30, 160) 0 batch_normalization_188[0][0]
__________________________________________________________________________________________________
conv2d_217 (Conv2D) (None, 30, 30, 192) 208896 block17_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_220 (Conv2D) (None, 30, 30, 192) 215040 activation_188[0][0]
__________________________________________________________________________________________________
batch_normalization_186 (BatchN (None, 30, 30, 192) 576 conv2d_217[0][0]
__________________________________________________________________________________________________
batch_normalization_189 (BatchN (None, 30, 30, 192) 576 conv2d_220[0][0]
__________________________________________________________________________________________________
activation_186 (Activation) (None, 30, 30, 192) 0 batch_normalization_186[0][0]
__________________________________________________________________________________________________
activation_189 (Activation) (None, 30, 30, 192) 0 batch_normalization_189[0][0]
__________________________________________________________________________________________________
block17_4_mixed (Concatenate) (None, 30, 30, 384) 0 activation_186[0][0]
activation_189[0][0]
__________________________________________________________________________________________________
block17_4_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_4_mixed[0][0]
__________________________________________________________________________________________________
block17_4 (Lambda) (None, 30, 30, 1088) 0 block17_3_ac[0][0]
block17_4_conv[0][0]
__________________________________________________________________________________________________
block17_4_ac (Activation) (None, 30, 30, 1088) 0 block17_4[0][0]
__________________________________________________________________________________________________
conv2d_222 (Conv2D) (None, 30, 30, 128) 139264 block17_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_191 (BatchN (None, 30, 30, 128) 384 conv2d_222[0][0]
__________________________________________________________________________________________________
activation_191 (Activation) (None, 30, 30, 128) 0 batch_normalization_191[0][0]
__________________________________________________________________________________________________
conv2d_223 (Conv2D) (None, 30, 30, 160) 143360 activation_191[0][0]
__________________________________________________________________________________________________
batch_normalization_192 (BatchN (None, 30, 30, 160) 480 conv2d_223[0][0]
__________________________________________________________________________________________________
activation_192 (Activation) (None, 30, 30, 160) 0 batch_normalization_192[0][0]
__________________________________________________________________________________________________
conv2d_221 (Conv2D) (None, 30, 30, 192) 208896 block17_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_224 (Conv2D) (None, 30, 30, 192) 215040 activation_192[0][0]
__________________________________________________________________________________________________
batch_normalization_190 (BatchN (None, 30, 30, 192) 576 conv2d_221[0][0]
__________________________________________________________________________________________________
batch_normalization_193 (BatchN (None, 30, 30, 192) 576 conv2d_224[0][0]
__________________________________________________________________________________________________
activation_190 (Activation) (None, 30, 30, 192) 0 batch_normalization_190[0][0]
__________________________________________________________________________________________________
activation_193 (Activation) (None, 30, 30, 192) 0 batch_normalization_193[0][0]
__________________________________________________________________________________________________
block17_5_mixed (Concatenate) (None, 30, 30, 384) 0 activation_190[0][0]
activation_193[0][0]
__________________________________________________________________________________________________
block17_5_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_5_mixed[0][0]
__________________________________________________________________________________________________
block17_5 (Lambda) (None, 30, 30, 1088) 0 block17_4_ac[0][0]
block17_5_conv[0][0]
__________________________________________________________________________________________________
block17_5_ac (Activation) (None, 30, 30, 1088) 0 block17_5[0][0]
__________________________________________________________________________________________________
conv2d_226 (Conv2D) (None, 30, 30, 128) 139264 block17_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_195 (BatchN (None, 30, 30, 128) 384 conv2d_226[0][0]
__________________________________________________________________________________________________
activation_195 (Activation) (None, 30, 30, 128) 0 batch_normalization_195[0][0]
__________________________________________________________________________________________________
conv2d_227 (Conv2D) (None, 30, 30, 160) 143360 activation_195[0][0]
__________________________________________________________________________________________________
batch_normalization_196 (BatchN (None, 30, 30, 160) 480 conv2d_227[0][0]
__________________________________________________________________________________________________
activation_196 (Activation) (None, 30, 30, 160) 0 batch_normalization_196[0][0]
__________________________________________________________________________________________________
conv2d_225 (Conv2D) (None, 30, 30, 192) 208896 block17_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_228 (Conv2D) (None, 30, 30, 192) 215040 activation_196[0][0]
__________________________________________________________________________________________________
batch_normalization_194 (BatchN (None, 30, 30, 192) 576 conv2d_225[0][0]
__________________________________________________________________________________________________
batch_normalization_197 (BatchN (None, 30, 30, 192) 576 conv2d_228[0][0]
__________________________________________________________________________________________________
activation_194 (Activation) (None, 30, 30, 192) 0 batch_normalization_194[0][0]
__________________________________________________________________________________________________
activation_197 (Activation) (None, 30, 30, 192) 0 batch_normalization_197[0][0]
__________________________________________________________________________________________________
block17_6_mixed (Concatenate) (None, 30, 30, 384) 0 activation_194[0][0]
activation_197[0][0]
__________________________________________________________________________________________________
block17_6_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_6_mixed[0][0]
__________________________________________________________________________________________________
block17_6 (Lambda) (None, 30, 30, 1088) 0 block17_5_ac[0][0]
block17_6_conv[0][0]
__________________________________________________________________________________________________
block17_6_ac (Activation) (None, 30, 30, 1088) 0 block17_6[0][0]
__________________________________________________________________________________________________
conv2d_230 (Conv2D) (None, 30, 30, 128) 139264 block17_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_199 (BatchN (None, 30, 30, 128) 384 conv2d_230[0][0]
__________________________________________________________________________________________________
activation_199 (Activation) (None, 30, 30, 128) 0 batch_normalization_199[0][0]
__________________________________________________________________________________________________
conv2d_231 (Conv2D) (None, 30, 30, 160) 143360 activation_199[0][0]
__________________________________________________________________________________________________
batch_normalization_200 (BatchN (None, 30, 30, 160) 480 conv2d_231[0][0]
__________________________________________________________________________________________________
activation_200 (Activation) (None, 30, 30, 160) 0 batch_normalization_200[0][0]
__________________________________________________________________________________________________
conv2d_229 (Conv2D) (None, 30, 30, 192) 208896 block17_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_232 (Conv2D) (None, 30, 30, 192) 215040 activation_200[0][0]
__________________________________________________________________________________________________
batch_normalization_198 (BatchN (None, 30, 30, 192) 576 conv2d_229[0][0]
__________________________________________________________________________________________________
batch_normalization_201 (BatchN (None, 30, 30, 192) 576 conv2d_232[0][0]
__________________________________________________________________________________________________
activation_198 (Activation) (None, 30, 30, 192) 0 batch_normalization_198[0][0]
__________________________________________________________________________________________________
activation_201 (Activation) (None, 30, 30, 192) 0 batch_normalization_201[0][0]
__________________________________________________________________________________________________
block17_7_mixed (Concatenate) (None, 30, 30, 384) 0 activation_198[0][0]
activation_201[0][0]
__________________________________________________________________________________________________
block17_7_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_7_mixed[0][0]
__________________________________________________________________________________________________
block17_7 (Lambda) (None, 30, 30, 1088) 0 block17_6_ac[0][0]
block17_7_conv[0][0]
__________________________________________________________________________________________________
block17_7_ac (Activation) (None, 30, 30, 1088) 0 block17_7[0][0]
__________________________________________________________________________________________________
conv2d_234 (Conv2D) (None, 30, 30, 128) 139264 block17_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_203 (BatchN (None, 30, 30, 128) 384 conv2d_234[0][0]
__________________________________________________________________________________________________
activation_203 (Activation) (None, 30, 30, 128) 0 batch_normalization_203[0][0]
__________________________________________________________________________________________________
conv2d_235 (Conv2D) (None, 30, 30, 160) 143360 activation_203[0][0]
__________________________________________________________________________________________________
batch_normalization_204 (BatchN (None, 30, 30, 160) 480 conv2d_235[0][0]
__________________________________________________________________________________________________
activation_204 (Activation) (None, 30, 30, 160) 0 batch_normalization_204[0][0]
__________________________________________________________________________________________________
conv2d_233 (Conv2D) (None, 30, 30, 192) 208896 block17_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_236 (Conv2D) (None, 30, 30, 192) 215040 activation_204[0][0]
__________________________________________________________________________________________________
batch_normalization_202 (BatchN (None, 30, 30, 192) 576 conv2d_233[0][0]
__________________________________________________________________________________________________
batch_normalization_205 (BatchN (None, 30, 30, 192) 576 conv2d_236[0][0]
__________________________________________________________________________________________________
activation_202 (Activation) (None, 30, 30, 192) 0 batch_normalization_202[0][0]
__________________________________________________________________________________________________
activation_205 (Activation) (None, 30, 30, 192) 0 batch_normalization_205[0][0]
__________________________________________________________________________________________________
block17_8_mixed (Concatenate) (None, 30, 30, 384) 0 activation_202[0][0]
activation_205[0][0]
__________________________________________________________________________________________________
block17_8_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_8_mixed[0][0]
__________________________________________________________________________________________________
block17_8 (Lambda) (None, 30, 30, 1088) 0 block17_7_ac[0][0]
block17_8_conv[0][0]
__________________________________________________________________________________________________
block17_8_ac (Activation) (None, 30, 30, 1088) 0 block17_8[0][0]
__________________________________________________________________________________________________
conv2d_238 (Conv2D) (None, 30, 30, 128) 139264 block17_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_207 (BatchN (None, 30, 30, 128) 384 conv2d_238[0][0]
__________________________________________________________________________________________________
activation_207 (Activation) (None, 30, 30, 128) 0 batch_normalization_207[0][0]
__________________________________________________________________________________________________
conv2d_239 (Conv2D) (None, 30, 30, 160) 143360 activation_207[0][0]
__________________________________________________________________________________________________
batch_normalization_208 (BatchN (None, 30, 30, 160) 480 conv2d_239[0][0]
__________________________________________________________________________________________________
activation_208 (Activation) (None, 30, 30, 160) 0 batch_normalization_208[0][0]
__________________________________________________________________________________________________
conv2d_237 (Conv2D) (None, 30, 30, 192) 208896 block17_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_240 (Conv2D) (None, 30, 30, 192) 215040 activation_208[0][0]
__________________________________________________________________________________________________
batch_normalization_206 (BatchN (None, 30, 30, 192) 576 conv2d_237[0][0]
__________________________________________________________________________________________________
batch_normalization_209 (BatchN (None, 30, 30, 192) 576 conv2d_240[0][0]
__________________________________________________________________________________________________
activation_206 (Activation) (None, 30, 30, 192) 0 batch_normalization_206[0][0]
__________________________________________________________________________________________________
activation_209 (Activation) (None, 30, 30, 192) 0 batch_normalization_209[0][0]
__________________________________________________________________________________________________
block17_9_mixed (Concatenate) (None, 30, 30, 384) 0 activation_206[0][0]
activation_209[0][0]
__________________________________________________________________________________________________
block17_9_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_9_mixed[0][0]
__________________________________________________________________________________________________
block17_9 (Lambda) (None, 30, 30, 1088) 0 block17_8_ac[0][0]
block17_9_conv[0][0]
__________________________________________________________________________________________________
block17_9_ac (Activation) (None, 30, 30, 1088) 0 block17_9[0][0]
__________________________________________________________________________________________________
conv2d_242 (Conv2D) (None, 30, 30, 128) 139264 block17_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_211 (BatchN (None, 30, 30, 128) 384 conv2d_242[0][0]
__________________________________________________________________________________________________
activation_211 (Activation) (None, 30, 30, 128) 0 batch_normalization_211[0][0]
__________________________________________________________________________________________________
conv2d_243 (Conv2D) (None, 30, 30, 160) 143360 activation_211[0][0]
__________________________________________________________________________________________________
batch_normalization_212 (BatchN (None, 30, 30, 160) 480 conv2d_243[0][0]
__________________________________________________________________________________________________
activation_212 (Activation) (None, 30, 30, 160) 0 batch_normalization_212[0][0]
__________________________________________________________________________________________________
conv2d_241 (Conv2D) (None, 30, 30, 192) 208896 block17_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_244 (Conv2D) (None, 30, 30, 192) 215040 activation_212[0][0]
__________________________________________________________________________________________________
batch_normalization_210 (BatchN (None, 30, 30, 192) 576 conv2d_241[0][0]
__________________________________________________________________________________________________
batch_normalization_213 (BatchN (None, 30, 30, 192) 576 conv2d_244[0][0]
__________________________________________________________________________________________________
activation_210 (Activation) (None, 30, 30, 192) 0 batch_normalization_210[0][0]
__________________________________________________________________________________________________
activation_213 (Activation) (None, 30, 30, 192) 0 batch_normalization_213[0][0]
__________________________________________________________________________________________________
block17_10_mixed (Concatenate) (None, 30, 30, 384) 0 activation_210[0][0]
activation_213[0][0]
__________________________________________________________________________________________________
block17_10_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_10_mixed[0][0]
__________________________________________________________________________________________________
block17_10 (Lambda) (None, 30, 30, 1088) 0 block17_9_ac[0][0]
block17_10_conv[0][0]
__________________________________________________________________________________________________
block17_10_ac (Activation) (None, 30, 30, 1088) 0 block17_10[0][0]
__________________________________________________________________________________________________
conv2d_246 (Conv2D) (None, 30, 30, 128) 139264 block17_10_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_215 (BatchN (None, 30, 30, 128) 384 conv2d_246[0][0]
__________________________________________________________________________________________________
activation_215 (Activation) (None, 30, 30, 128) 0 batch_normalization_215[0][0]
__________________________________________________________________________________________________
conv2d_247 (Conv2D) (None, 30, 30, 160) 143360 activation_215[0][0]
__________________________________________________________________________________________________
batch_normalization_216 (BatchN (None, 30, 30, 160) 480 conv2d_247[0][0]
__________________________________________________________________________________________________
activation_216 (Activation) (None, 30, 30, 160) 0 batch_normalization_216[0][0]
__________________________________________________________________________________________________
conv2d_245 (Conv2D) (None, 30, 30, 192) 208896 block17_10_ac[0][0]
__________________________________________________________________________________________________
conv2d_248 (Conv2D) (None, 30, 30, 192) 215040 activation_216[0][0]
__________________________________________________________________________________________________
batch_normalization_214 (BatchN (None, 30, 30, 192) 576 conv2d_245[0][0]
__________________________________________________________________________________________________
batch_normalization_217 (BatchN (None, 30, 30, 192) 576 conv2d_248[0][0]
__________________________________________________________________________________________________
activation_214 (Activation) (None, 30, 30, 192) 0 batch_normalization_214[0][0]
__________________________________________________________________________________________________
activation_217 (Activation) (None, 30, 30, 192) 0 batch_normalization_217[0][0]
__________________________________________________________________________________________________
block17_11_mixed (Concatenate) (None, 30, 30, 384) 0 activation_214[0][0]
activation_217[0][0]
__________________________________________________________________________________________________
block17_11_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_11_mixed[0][0]
__________________________________________________________________________________________________
block17_11 (Lambda) (None, 30, 30, 1088) 0 block17_10_ac[0][0]
block17_11_conv[0][0]
__________________________________________________________________________________________________
block17_11_ac (Activation) (None, 30, 30, 1088) 0 block17_11[0][0]
__________________________________________________________________________________________________
conv2d_250 (Conv2D) (None, 30, 30, 128) 139264 block17_11_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_219 (BatchN (None, 30, 30, 128) 384 conv2d_250[0][0]
__________________________________________________________________________________________________
activation_219 (Activation) (None, 30, 30, 128) 0 batch_normalization_219[0][0]
__________________________________________________________________________________________________
conv2d_251 (Conv2D) (None, 30, 30, 160) 143360 activation_219[0][0]
__________________________________________________________________________________________________
batch_normalization_220 (BatchN (None, 30, 30, 160) 480 conv2d_251[0][0]
__________________________________________________________________________________________________
activation_220 (Activation) (None, 30, 30, 160) 0 batch_normalization_220[0][0]
__________________________________________________________________________________________________
conv2d_249 (Conv2D) (None, 30, 30, 192) 208896 block17_11_ac[0][0]
__________________________________________________________________________________________________
conv2d_252 (Conv2D) (None, 30, 30, 192) 215040 activation_220[0][0]
__________________________________________________________________________________________________
batch_normalization_218 (BatchN (None, 30, 30, 192) 576 conv2d_249[0][0]
__________________________________________________________________________________________________
batch_normalization_221 (BatchN (None, 30, 30, 192) 576 conv2d_252[0][0]
__________________________________________________________________________________________________
activation_218 (Activation) (None, 30, 30, 192) 0 batch_normalization_218[0][0]
__________________________________________________________________________________________________
activation_221 (Activation) (None, 30, 30, 192) 0 batch_normalization_221[0][0]
__________________________________________________________________________________________________
block17_12_mixed (Concatenate) (None, 30, 30, 384) 0 activation_218[0][0]
activation_221[0][0]
__________________________________________________________________________________________________
block17_12_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_12_mixed[0][0]
__________________________________________________________________________________________________
block17_12 (Lambda) (None, 30, 30, 1088) 0 block17_11_ac[0][0]
block17_12_conv[0][0]
__________________________________________________________________________________________________
block17_12_ac (Activation) (None, 30, 30, 1088) 0 block17_12[0][0]
__________________________________________________________________________________________________
conv2d_254 (Conv2D) (None, 30, 30, 128) 139264 block17_12_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_223 (BatchN (None, 30, 30, 128) 384 conv2d_254[0][0]
__________________________________________________________________________________________________
activation_223 (Activation) (None, 30, 30, 128) 0 batch_normalization_223[0][0]
__________________________________________________________________________________________________
conv2d_255 (Conv2D) (None, 30, 30, 160) 143360 activation_223[0][0]
__________________________________________________________________________________________________
batch_normalization_224 (BatchN (None, 30, 30, 160) 480 conv2d_255[0][0]
__________________________________________________________________________________________________
activation_224 (Activation) (None, 30, 30, 160) 0 batch_normalization_224[0][0]
__________________________________________________________________________________________________
conv2d_253 (Conv2D) (None, 30, 30, 192) 208896 block17_12_ac[0][0]
__________________________________________________________________________________________________
conv2d_256 (Conv2D) (None, 30, 30, 192) 215040 activation_224[0][0]
__________________________________________________________________________________________________
batch_normalization_222 (BatchN (None, 30, 30, 192) 576 conv2d_253[0][0]
__________________________________________________________________________________________________
batch_normalization_225 (BatchN (None, 30, 30, 192) 576 conv2d_256[0][0]
__________________________________________________________________________________________________
activation_222 (Activation) (None, 30, 30, 192) 0 batch_normalization_222[0][0]
__________________________________________________________________________________________________
activation_225 (Activation) (None, 30, 30, 192) 0 batch_normalization_225[0][0]
__________________________________________________________________________________________________
block17_13_mixed (Concatenate) (None, 30, 30, 384) 0 activation_222[0][0]
activation_225[0][0]
__________________________________________________________________________________________________
block17_13_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_13_mixed[0][0]
__________________________________________________________________________________________________
block17_13 (Lambda) (None, 30, 30, 1088) 0 block17_12_ac[0][0]
block17_13_conv[0][0]
__________________________________________________________________________________________________
block17_13_ac (Activation) (None, 30, 30, 1088) 0 block17_13[0][0]
__________________________________________________________________________________________________
conv2d_258 (Conv2D) (None, 30, 30, 128) 139264 block17_13_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_227 (BatchN (None, 30, 30, 128) 384 conv2d_258[0][0]
__________________________________________________________________________________________________
activation_227 (Activation) (None, 30, 30, 128) 0 batch_normalization_227[0][0]
__________________________________________________________________________________________________
conv2d_259 (Conv2D) (None, 30, 30, 160) 143360 activation_227[0][0]
__________________________________________________________________________________________________
batch_normalization_228 (BatchN (None, 30, 30, 160) 480 conv2d_259[0][0]
__________________________________________________________________________________________________
activation_228 (Activation) (None, 30, 30, 160) 0 batch_normalization_228[0][0]
__________________________________________________________________________________________________
conv2d_257 (Conv2D) (None, 30, 30, 192) 208896 block17_13_ac[0][0]
__________________________________________________________________________________________________
conv2d_260 (Conv2D) (None, 30, 30, 192) 215040 activation_228[0][0]
__________________________________________________________________________________________________
batch_normalization_226 (BatchN (None, 30, 30, 192) 576 conv2d_257[0][0]
__________________________________________________________________________________________________
batch_normalization_229 (BatchN (None, 30, 30, 192) 576 conv2d_260[0][0]
__________________________________________________________________________________________________
activation_226 (Activation) (None, 30, 30, 192) 0 batch_normalization_226[0][0]
__________________________________________________________________________________________________
activation_229 (Activation) (None, 30, 30, 192) 0 batch_normalization_229[0][0]
__________________________________________________________________________________________________
block17_14_mixed (Concatenate) (None, 30, 30, 384) 0 activation_226[0][0]
activation_229[0][0]
__________________________________________________________________________________________________
block17_14_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_14_mixed[0][0]
__________________________________________________________________________________________________
block17_14 (Lambda) (None, 30, 30, 1088) 0 block17_13_ac[0][0]
block17_14_conv[0][0]
__________________________________________________________________________________________________
block17_14_ac (Activation) (None, 30, 30, 1088) 0 block17_14[0][0]
__________________________________________________________________________________________________
conv2d_262 (Conv2D) (None, 30, 30, 128) 139264 block17_14_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_231 (BatchN (None, 30, 30, 128) 384 conv2d_262[0][0]
__________________________________________________________________________________________________
activation_231 (Activation) (None, 30, 30, 128) 0 batch_normalization_231[0][0]
__________________________________________________________________________________________________
conv2d_263 (Conv2D) (None, 30, 30, 160) 143360 activation_231[0][0]
__________________________________________________________________________________________________
batch_normalization_232 (BatchN (None, 30, 30, 160) 480 conv2d_263[0][0]
__________________________________________________________________________________________________
activation_232 (Activation) (None, 30, 30, 160) 0 batch_normalization_232[0][0]
__________________________________________________________________________________________________
conv2d_261 (Conv2D) (None, 30, 30, 192) 208896 block17_14_ac[0][0]
__________________________________________________________________________________________________
conv2d_264 (Conv2D) (None, 30, 30, 192) 215040 activation_232[0][0]
__________________________________________________________________________________________________
batch_normalization_230 (BatchN (None, 30, 30, 192) 576 conv2d_261[0][0]
__________________________________________________________________________________________________
batch_normalization_233 (BatchN (None, 30, 30, 192) 576 conv2d_264[0][0]
__________________________________________________________________________________________________
activation_230 (Activation) (None, 30, 30, 192) 0 batch_normalization_230[0][0]
__________________________________________________________________________________________________
activation_233 (Activation) (None, 30, 30, 192) 0 batch_normalization_233[0][0]
__________________________________________________________________________________________________
block17_15_mixed (Concatenate) (None, 30, 30, 384) 0 activation_230[0][0]
activation_233[0][0]
__________________________________________________________________________________________________
block17_15_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_15_mixed[0][0]
__________________________________________________________________________________________________
block17_15 (Lambda) (None, 30, 30, 1088) 0 block17_14_ac[0][0]
block17_15_conv[0][0]
__________________________________________________________________________________________________
block17_15_ac (Activation) (None, 30, 30, 1088) 0 block17_15[0][0]
__________________________________________________________________________________________________
conv2d_266 (Conv2D) (None, 30, 30, 128) 139264 block17_15_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_235 (BatchN (None, 30, 30, 128) 384 conv2d_266[0][0]
__________________________________________________________________________________________________
activation_235 (Activation) (None, 30, 30, 128) 0 batch_normalization_235[0][0]
__________________________________________________________________________________________________
conv2d_267 (Conv2D) (None, 30, 30, 160) 143360 activation_235[0][0]
__________________________________________________________________________________________________
batch_normalization_236 (BatchN (None, 30, 30, 160) 480 conv2d_267[0][0]
__________________________________________________________________________________________________
activation_236 (Activation) (None, 30, 30, 160) 0 batch_normalization_236[0][0]
__________________________________________________________________________________________________
conv2d_265 (Conv2D) (None, 30, 30, 192) 208896 block17_15_ac[0][0]
__________________________________________________________________________________________________
conv2d_268 (Conv2D) (None, 30, 30, 192) 215040 activation_236[0][0]
__________________________________________________________________________________________________
batch_normalization_234 (BatchN (None, 30, 30, 192) 576 conv2d_265[0][0]
__________________________________________________________________________________________________
batch_normalization_237 (BatchN (None, 30, 30, 192) 576 conv2d_268[0][0]
__________________________________________________________________________________________________
activation_234 (Activation) (None, 30, 30, 192) 0 batch_normalization_234[0][0]
__________________________________________________________________________________________________
activation_237 (Activation) (None, 30, 30, 192) 0 batch_normalization_237[0][0]
__________________________________________________________________________________________________
block17_16_mixed (Concatenate) (None, 30, 30, 384) 0 activation_234[0][0]
activation_237[0][0]
__________________________________________________________________________________________________
block17_16_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_16_mixed[0][0]
__________________________________________________________________________________________________
block17_16 (Lambda) (None, 30, 30, 1088) 0 block17_15_ac[0][0]
block17_16_conv[0][0]
__________________________________________________________________________________________________
block17_16_ac (Activation) (None, 30, 30, 1088) 0 block17_16[0][0]
__________________________________________________________________________________________________
conv2d_270 (Conv2D) (None, 30, 30, 128) 139264 block17_16_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_239 (BatchN (None, 30, 30, 128) 384 conv2d_270[0][0]
__________________________________________________________________________________________________
activation_239 (Activation) (None, 30, 30, 128) 0 batch_normalization_239[0][0]
__________________________________________________________________________________________________
conv2d_271 (Conv2D) (None, 30, 30, 160) 143360 activation_239[0][0]
__________________________________________________________________________________________________
batch_normalization_240 (BatchN (None, 30, 30, 160) 480 conv2d_271[0][0]
__________________________________________________________________________________________________
activation_240 (Activation) (None, 30, 30, 160) 0 batch_normalization_240[0][0]
__________________________________________________________________________________________________
conv2d_269 (Conv2D) (None, 30, 30, 192) 208896 block17_16_ac[0][0]
__________________________________________________________________________________________________
conv2d_272 (Conv2D) (None, 30, 30, 192) 215040 activation_240[0][0]
__________________________________________________________________________________________________
batch_normalization_238 (BatchN (None, 30, 30, 192) 576 conv2d_269[0][0]
__________________________________________________________________________________________________
batch_normalization_241 (BatchN (None, 30, 30, 192) 576 conv2d_272[0][0]
__________________________________________________________________________________________________
activation_238 (Activation) (None, 30, 30, 192) 0 batch_normalization_238[0][0]
__________________________________________________________________________________________________
activation_241 (Activation) (None, 30, 30, 192) 0 batch_normalization_241[0][0]
__________________________________________________________________________________________________
block17_17_mixed (Concatenate) (None, 30, 30, 384) 0 activation_238[0][0]
activation_241[0][0]
__________________________________________________________________________________________________
block17_17_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_17_mixed[0][0]
__________________________________________________________________________________________________
block17_17 (Lambda) (None, 30, 30, 1088) 0 block17_16_ac[0][0]
block17_17_conv[0][0]
__________________________________________________________________________________________________
block17_17_ac (Activation) (None, 30, 30, 1088) 0 block17_17[0][0]
__________________________________________________________________________________________________
conv2d_274 (Conv2D) (None, 30, 30, 128) 139264 block17_17_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_243 (BatchN (None, 30, 30, 128) 384 conv2d_274[0][0]
__________________________________________________________________________________________________
activation_243 (Activation) (None, 30, 30, 128) 0 batch_normalization_243[0][0]
__________________________________________________________________________________________________
conv2d_275 (Conv2D) (None, 30, 30, 160) 143360 activation_243[0][0]
__________________________________________________________________________________________________
batch_normalization_244 (BatchN (None, 30, 30, 160) 480 conv2d_275[0][0]
__________________________________________________________________________________________________
activation_244 (Activation) (None, 30, 30, 160) 0 batch_normalization_244[0][0]
__________________________________________________________________________________________________
conv2d_273 (Conv2D) (None, 30, 30, 192) 208896 block17_17_ac[0][0]
__________________________________________________________________________________________________
conv2d_276 (Conv2D) (None, 30, 30, 192) 215040 activation_244[0][0]
__________________________________________________________________________________________________
batch_normalization_242 (BatchN (None, 30, 30, 192) 576 conv2d_273[0][0]
__________________________________________________________________________________________________
batch_normalization_245 (BatchN (None, 30, 30, 192) 576 conv2d_276[0][0]
__________________________________________________________________________________________________
activation_242 (Activation) (None, 30, 30, 192) 0 batch_normalization_242[0][0]
__________________________________________________________________________________________________
activation_245 (Activation) (None, 30, 30, 192) 0 batch_normalization_245[0][0]
__________________________________________________________________________________________________
block17_18_mixed (Concatenate) (None, 30, 30, 384) 0 activation_242[0][0]
activation_245[0][0]
__________________________________________________________________________________________________
block17_18_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_18_mixed[0][0]
__________________________________________________________________________________________________
block17_18 (Lambda) (None, 30, 30, 1088) 0 block17_17_ac[0][0]
block17_18_conv[0][0]
__________________________________________________________________________________________________
block17_18_ac (Activation) (None, 30, 30, 1088) 0 block17_18[0][0]
__________________________________________________________________________________________________
conv2d_278 (Conv2D) (None, 30, 30, 128) 139264 block17_18_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_247 (BatchN (None, 30, 30, 128) 384 conv2d_278[0][0]
__________________________________________________________________________________________________
activation_247 (Activation) (None, 30, 30, 128) 0 batch_normalization_247[0][0]
__________________________________________________________________________________________________
conv2d_279 (Conv2D) (None, 30, 30, 160) 143360 activation_247[0][0]
__________________________________________________________________________________________________
batch_normalization_248 (BatchN (None, 30, 30, 160) 480 conv2d_279[0][0]
__________________________________________________________________________________________________
activation_248 (Activation) (None, 30, 30, 160) 0 batch_normalization_248[0][0]
__________________________________________________________________________________________________
conv2d_277 (Conv2D) (None, 30, 30, 192) 208896 block17_18_ac[0][0]
__________________________________________________________________________________________________
conv2d_280 (Conv2D) (None, 30, 30, 192) 215040 activation_248[0][0]
__________________________________________________________________________________________________
batch_normalization_246 (BatchN (None, 30, 30, 192) 576 conv2d_277[0][0]
__________________________________________________________________________________________________
batch_normalization_249 (BatchN (None, 30, 30, 192) 576 conv2d_280[0][0]
__________________________________________________________________________________________________
activation_246 (Activation) (None, 30, 30, 192) 0 batch_normalization_246[0][0]
__________________________________________________________________________________________________
activation_249 (Activation) (None, 30, 30, 192) 0 batch_normalization_249[0][0]
__________________________________________________________________________________________________
block17_19_mixed (Concatenate) (None, 30, 30, 384) 0 activation_246[0][0]
activation_249[0][0]
__________________________________________________________________________________________________
block17_19_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_19_mixed[0][0]
__________________________________________________________________________________________________
block17_19 (Lambda) (None, 30, 30, 1088) 0 block17_18_ac[0][0]
block17_19_conv[0][0]
__________________________________________________________________________________________________
block17_19_ac (Activation) (None, 30, 30, 1088) 0 block17_19[0][0]
__________________________________________________________________________________________________
conv2d_282 (Conv2D) (None, 30, 30, 128) 139264 block17_19_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_251 (BatchN (None, 30, 30, 128) 384 conv2d_282[0][0]
__________________________________________________________________________________________________
activation_251 (Activation) (None, 30, 30, 128) 0 batch_normalization_251[0][0]
__________________________________________________________________________________________________
conv2d_283 (Conv2D) (None, 30, 30, 160) 143360 activation_251[0][0]
__________________________________________________________________________________________________
batch_normalization_252 (BatchN (None, 30, 30, 160) 480 conv2d_283[0][0]
__________________________________________________________________________________________________
activation_252 (Activation) (None, 30, 30, 160) 0 batch_normalization_252[0][0]
__________________________________________________________________________________________________
conv2d_281 (Conv2D) (None, 30, 30, 192) 208896 block17_19_ac[0][0]
__________________________________________________________________________________________________
conv2d_284 (Conv2D) (None, 30, 30, 192) 215040 activation_252[0][0]
__________________________________________________________________________________________________
batch_normalization_250 (BatchN (None, 30, 30, 192) 576 conv2d_281[0][0]
__________________________________________________________________________________________________
batch_normalization_253 (BatchN (None, 30, 30, 192) 576 conv2d_284[0][0]
__________________________________________________________________________________________________
activation_250 (Activation) (None, 30, 30, 192) 0 batch_normalization_250[0][0]
__________________________________________________________________________________________________
activation_253 (Activation) (None, 30, 30, 192) 0 batch_normalization_253[0][0]
__________________________________________________________________________________________________
block17_20_mixed (Concatenate) (None, 30, 30, 384) 0 activation_250[0][0]
activation_253[0][0]
__________________________________________________________________________________________________
block17_20_conv (Conv2D) (None, 30, 30, 1088) 418880 block17_20_mixed[0][0]
__________________________________________________________________________________________________
block17_20 (Lambda) (None, 30, 30, 1088) 0 block17_19_ac[0][0]
block17_20_conv[0][0]
__________________________________________________________________________________________________
block17_20_ac (Activation) (None, 30, 30, 1088) 0 block17_20[0][0]
__________________________________________________________________________________________________
conv2d_289 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_258 (BatchN (None, 30, 30, 256) 768 conv2d_289[0][0]
__________________________________________________________________________________________________
activation_258 (Activation) (None, 30, 30, 256) 0 batch_normalization_258[0][0]
__________________________________________________________________________________________________
conv2d_285 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
conv2d_287 (Conv2D) (None, 30, 30, 256) 278528 block17_20_ac[0][0]
__________________________________________________________________________________________________
conv2d_290 (Conv2D) (None, 30, 30, 288) 663552 activation_258[0][0]
__________________________________________________________________________________________________
batch_normalization_254 (BatchN (None, 30, 30, 256) 768 conv2d_285[0][0]
__________________________________________________________________________________________________
batch_normalization_256 (BatchN (None, 30, 30, 256) 768 conv2d_287[0][0]
__________________________________________________________________________________________________
batch_normalization_259 (BatchN (None, 30, 30, 288) 864 conv2d_290[0][0]
__________________________________________________________________________________________________
activation_254 (Activation) (None, 30, 30, 256) 0 batch_normalization_254[0][0]
__________________________________________________________________________________________________
activation_256 (Activation) (None, 30, 30, 256) 0 batch_normalization_256[0][0]
__________________________________________________________________________________________________
activation_259 (Activation) (None, 30, 30, 288) 0 batch_normalization_259[0][0]
__________________________________________________________________________________________________
conv2d_286 (Conv2D) (None, 14, 14, 384) 884736 activation_254[0][0]
__________________________________________________________________________________________________
conv2d_288 (Conv2D) (None, 14, 14, 288) 663552 activation_256[0][0]
__________________________________________________________________________________________________
conv2d_291 (Conv2D) (None, 14, 14, 320) 829440 activation_259[0][0]
__________________________________________________________________________________________________
batch_normalization_255 (BatchN (None, 14, 14, 384) 1152 conv2d_286[0][0]
__________________________________________________________________________________________________
batch_normalization_257 (BatchN (None, 14, 14, 288) 864 conv2d_288[0][0]
__________________________________________________________________________________________________
batch_normalization_260 (BatchN (None, 14, 14, 320) 960 conv2d_291[0][0]
__________________________________________________________________________________________________
activation_255 (Activation) (None, 14, 14, 384) 0 batch_normalization_255[0][0]
__________________________________________________________________________________________________
activation_257 (Activation) (None, 14, 14, 288) 0 batch_normalization_257[0][0]
__________________________________________________________________________________________________
activation_260 (Activation) (None, 14, 14, 320) 0 batch_normalization_260[0][0]
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 14, 14, 1088) 0 block17_20_ac[0][0]
__________________________________________________________________________________________________
mixed_7a (Concatenate) (None, 14, 14, 2080) 0 activation_255[0][0]
activation_257[0][0]
activation_260[0][0]
max_pooling2d_17[0][0]
__________________________________________________________________________________________________
conv2d_293 (Conv2D) (None, 14, 14, 192) 399360 mixed_7a[0][0]
__________________________________________________________________________________________________
batch_normalization_262 (BatchN (None, 14, 14, 192) 576 conv2d_293[0][0]
__________________________________________________________________________________________________
activation_262 (Activation) (None, 14, 14, 192) 0 batch_normalization_262[0][0]
__________________________________________________________________________________________________
conv2d_294 (Conv2D) (None, 14, 14, 224) 129024 activation_262[0][0]
__________________________________________________________________________________________________
batch_normalization_263 (BatchN (None, 14, 14, 224) 672 conv2d_294[0][0]
__________________________________________________________________________________________________
activation_263 (Activation) (None, 14, 14, 224) 0 batch_normalization_263[0][0]
__________________________________________________________________________________________________
conv2d_292 (Conv2D) (None, 14, 14, 192) 399360 mixed_7a[0][0]
__________________________________________________________________________________________________
conv2d_295 (Conv2D) (None, 14, 14, 256) 172032 activation_263[0][0]
__________________________________________________________________________________________________
batch_normalization_261 (BatchN (None, 14, 14, 192) 576 conv2d_292[0][0]
__________________________________________________________________________________________________
batch_normalization_264 (BatchN (None, 14, 14, 256) 768 conv2d_295[0][0]
__________________________________________________________________________________________________
activation_261 (Activation) (None, 14, 14, 192) 0 batch_normalization_261[0][0]
__________________________________________________________________________________________________
activation_264 (Activation) (None, 14, 14, 256) 0 batch_normalization_264[0][0]
__________________________________________________________________________________________________
block8_1_mixed (Concatenate) (None, 14, 14, 448) 0 activation_261[0][0]
activation_264[0][0]
__________________________________________________________________________________________________
block8_1_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_1_mixed[0][0]
__________________________________________________________________________________________________
block8_1 (Lambda) (None, 14, 14, 2080) 0 mixed_7a[0][0]
block8_1_conv[0][0]
__________________________________________________________________________________________________
block8_1_ac (Activation) (None, 14, 14, 2080) 0 block8_1[0][0]
__________________________________________________________________________________________________
conv2d_297 (Conv2D) (None, 14, 14, 192) 399360 block8_1_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_266 (BatchN (None, 14, 14, 192) 576 conv2d_297[0][0]
__________________________________________________________________________________________________
activation_266 (Activation) (None, 14, 14, 192) 0 batch_normalization_266[0][0]
__________________________________________________________________________________________________
conv2d_298 (Conv2D) (None, 14, 14, 224) 129024 activation_266[0][0]
__________________________________________________________________________________________________
batch_normalization_267 (BatchN (None, 14, 14, 224) 672 conv2d_298[0][0]
__________________________________________________________________________________________________
activation_267 (Activation) (None, 14, 14, 224) 0 batch_normalization_267[0][0]
__________________________________________________________________________________________________
conv2d_296 (Conv2D) (None, 14, 14, 192) 399360 block8_1_ac[0][0]
__________________________________________________________________________________________________
conv2d_299 (Conv2D) (None, 14, 14, 256) 172032 activation_267[0][0]
__________________________________________________________________________________________________
batch_normalization_265 (BatchN (None, 14, 14, 192) 576 conv2d_296[0][0]
__________________________________________________________________________________________________
batch_normalization_268 (BatchN (None, 14, 14, 256) 768 conv2d_299[0][0]
__________________________________________________________________________________________________
activation_265 (Activation) (None, 14, 14, 192) 0 batch_normalization_265[0][0]
__________________________________________________________________________________________________
activation_268 (Activation) (None, 14, 14, 256) 0 batch_normalization_268[0][0]
__________________________________________________________________________________________________
block8_2_mixed (Concatenate) (None, 14, 14, 448) 0 activation_265[0][0]
activation_268[0][0]
__________________________________________________________________________________________________
block8_2_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_2_mixed[0][0]
__________________________________________________________________________________________________
block8_2 (Lambda) (None, 14, 14, 2080) 0 block8_1_ac[0][0]
block8_2_conv[0][0]
__________________________________________________________________________________________________
block8_2_ac (Activation) (None, 14, 14, 2080) 0 block8_2[0][0]
__________________________________________________________________________________________________
conv2d_301 (Conv2D) (None, 14, 14, 192) 399360 block8_2_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_270 (BatchN (None, 14, 14, 192) 576 conv2d_301[0][0]
__________________________________________________________________________________________________
activation_270 (Activation) (None, 14, 14, 192) 0 batch_normalization_270[0][0]
__________________________________________________________________________________________________
conv2d_302 (Conv2D) (None, 14, 14, 224) 129024 activation_270[0][0]
__________________________________________________________________________________________________
batch_normalization_271 (BatchN (None, 14, 14, 224) 672 conv2d_302[0][0]
__________________________________________________________________________________________________
activation_271 (Activation) (None, 14, 14, 224) 0 batch_normalization_271[0][0]
__________________________________________________________________________________________________
conv2d_300 (Conv2D) (None, 14, 14, 192) 399360 block8_2_ac[0][0]
__________________________________________________________________________________________________
conv2d_303 (Conv2D) (None, 14, 14, 256) 172032 activation_271[0][0]
__________________________________________________________________________________________________
batch_normalization_269 (BatchN (None, 14, 14, 192) 576 conv2d_300[0][0]
__________________________________________________________________________________________________
batch_normalization_272 (BatchN (None, 14, 14, 256) 768 conv2d_303[0][0]
__________________________________________________________________________________________________
activation_269 (Activation) (None, 14, 14, 192) 0 batch_normalization_269[0][0]
__________________________________________________________________________________________________
activation_272 (Activation) (None, 14, 14, 256) 0 batch_normalization_272[0][0]
__________________________________________________________________________________________________
block8_3_mixed (Concatenate) (None, 14, 14, 448) 0 activation_269[0][0]
activation_272[0][0]
__________________________________________________________________________________________________
block8_3_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_3_mixed[0][0]
__________________________________________________________________________________________________
block8_3 (Lambda) (None, 14, 14, 2080) 0 block8_2_ac[0][0]
block8_3_conv[0][0]
__________________________________________________________________________________________________
block8_3_ac (Activation) (None, 14, 14, 2080) 0 block8_3[0][0]
__________________________________________________________________________________________________
conv2d_305 (Conv2D) (None, 14, 14, 192) 399360 block8_3_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_274 (BatchN (None, 14, 14, 192) 576 conv2d_305[0][0]
__________________________________________________________________________________________________
activation_274 (Activation) (None, 14, 14, 192) 0 batch_normalization_274[0][0]
__________________________________________________________________________________________________
conv2d_306 (Conv2D) (None, 14, 14, 224) 129024 activation_274[0][0]
__________________________________________________________________________________________________
batch_normalization_275 (BatchN (None, 14, 14, 224) 672 conv2d_306[0][0]
__________________________________________________________________________________________________
activation_275 (Activation) (None, 14, 14, 224) 0 batch_normalization_275[0][0]
__________________________________________________________________________________________________
conv2d_304 (Conv2D) (None, 14, 14, 192) 399360 block8_3_ac[0][0]
__________________________________________________________________________________________________
conv2d_307 (Conv2D) (None, 14, 14, 256) 172032 activation_275[0][0]
__________________________________________________________________________________________________
batch_normalization_273 (BatchN (None, 14, 14, 192) 576 conv2d_304[0][0]
__________________________________________________________________________________________________
batch_normalization_276 (BatchN (None, 14, 14, 256) 768 conv2d_307[0][0]
__________________________________________________________________________________________________
activation_273 (Activation) (None, 14, 14, 192) 0 batch_normalization_273[0][0]
__________________________________________________________________________________________________
activation_276 (Activation) (None, 14, 14, 256) 0 batch_normalization_276[0][0]
__________________________________________________________________________________________________
block8_4_mixed (Concatenate) (None, 14, 14, 448) 0 activation_273[0][0]
activation_276[0][0]
__________________________________________________________________________________________________
block8_4_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_4_mixed[0][0]
__________________________________________________________________________________________________
block8_4 (Lambda) (None, 14, 14, 2080) 0 block8_3_ac[0][0]
block8_4_conv[0][0]
__________________________________________________________________________________________________
block8_4_ac (Activation) (None, 14, 14, 2080) 0 block8_4[0][0]
__________________________________________________________________________________________________
conv2d_309 (Conv2D) (None, 14, 14, 192) 399360 block8_4_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_278 (BatchN (None, 14, 14, 192) 576 conv2d_309[0][0]
__________________________________________________________________________________________________
activation_278 (Activation) (None, 14, 14, 192) 0 batch_normalization_278[0][0]
__________________________________________________________________________________________________
conv2d_310 (Conv2D) (None, 14, 14, 224) 129024 activation_278[0][0]
__________________________________________________________________________________________________
batch_normalization_279 (BatchN (None, 14, 14, 224) 672 conv2d_310[0][0]
__________________________________________________________________________________________________
activation_279 (Activation) (None, 14, 14, 224) 0 batch_normalization_279[0][0]
__________________________________________________________________________________________________
conv2d_308 (Conv2D) (None, 14, 14, 192) 399360 block8_4_ac[0][0]
__________________________________________________________________________________________________
conv2d_311 (Conv2D) (None, 14, 14, 256) 172032 activation_279[0][0]
__________________________________________________________________________________________________
batch_normalization_277 (BatchN (None, 14, 14, 192) 576 conv2d_308[0][0]
__________________________________________________________________________________________________
batch_normalization_280 (BatchN (None, 14, 14, 256) 768 conv2d_311[0][0]
__________________________________________________________________________________________________
activation_277 (Activation) (None, 14, 14, 192) 0 batch_normalization_277[0][0]
__________________________________________________________________________________________________
activation_280 (Activation) (None, 14, 14, 256) 0 batch_normalization_280[0][0]
__________________________________________________________________________________________________
block8_5_mixed (Concatenate) (None, 14, 14, 448) 0 activation_277[0][0]
activation_280[0][0]
__________________________________________________________________________________________________
block8_5_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_5_mixed[0][0]
__________________________________________________________________________________________________
block8_5 (Lambda) (None, 14, 14, 2080) 0 block8_4_ac[0][0]
block8_5_conv[0][0]
__________________________________________________________________________________________________
block8_5_ac (Activation) (None, 14, 14, 2080) 0 block8_5[0][0]
__________________________________________________________________________________________________
conv2d_313 (Conv2D) (None, 14, 14, 192) 399360 block8_5_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_282 (BatchN (None, 14, 14, 192) 576 conv2d_313[0][0]
__________________________________________________________________________________________________
activation_282 (Activation) (None, 14, 14, 192) 0 batch_normalization_282[0][0]
__________________________________________________________________________________________________
conv2d_314 (Conv2D) (None, 14, 14, 224) 129024 activation_282[0][0]
__________________________________________________________________________________________________
batch_normalization_283 (BatchN (None, 14, 14, 224) 672 conv2d_314[0][0]
__________________________________________________________________________________________________
activation_283 (Activation) (None, 14, 14, 224) 0 batch_normalization_283[0][0]
__________________________________________________________________________________________________
conv2d_312 (Conv2D) (None, 14, 14, 192) 399360 block8_5_ac[0][0]
__________________________________________________________________________________________________
conv2d_315 (Conv2D) (None, 14, 14, 256) 172032 activation_283[0][0]
__________________________________________________________________________________________________
batch_normalization_281 (BatchN (None, 14, 14, 192) 576 conv2d_312[0][0]
__________________________________________________________________________________________________
batch_normalization_284 (BatchN (None, 14, 14, 256) 768 conv2d_315[0][0]
__________________________________________________________________________________________________
activation_281 (Activation) (None, 14, 14, 192) 0 batch_normalization_281[0][0]
__________________________________________________________________________________________________
activation_284 (Activation) (None, 14, 14, 256) 0 batch_normalization_284[0][0]
__________________________________________________________________________________________________
block8_6_mixed (Concatenate) (None, 14, 14, 448) 0 activation_281[0][0]
activation_284[0][0]
__________________________________________________________________________________________________
block8_6_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_6_mixed[0][0]
__________________________________________________________________________________________________
block8_6 (Lambda) (None, 14, 14, 2080) 0 block8_5_ac[0][0]
block8_6_conv[0][0]
__________________________________________________________________________________________________
block8_6_ac (Activation) (None, 14, 14, 2080) 0 block8_6[0][0]
__________________________________________________________________________________________________
conv2d_317 (Conv2D) (None, 14, 14, 192) 399360 block8_6_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_286 (BatchN (None, 14, 14, 192) 576 conv2d_317[0][0]
__________________________________________________________________________________________________
activation_286 (Activation) (None, 14, 14, 192) 0 batch_normalization_286[0][0]
__________________________________________________________________________________________________
conv2d_318 (Conv2D) (None, 14, 14, 224) 129024 activation_286[0][0]
__________________________________________________________________________________________________
batch_normalization_287 (BatchN (None, 14, 14, 224) 672 conv2d_318[0][0]
__________________________________________________________________________________________________
activation_287 (Activation) (None, 14, 14, 224) 0 batch_normalization_287[0][0]
__________________________________________________________________________________________________
conv2d_316 (Conv2D) (None, 14, 14, 192) 399360 block8_6_ac[0][0]
__________________________________________________________________________________________________
conv2d_319 (Conv2D) (None, 14, 14, 256) 172032 activation_287[0][0]
__________________________________________________________________________________________________
batch_normalization_285 (BatchN (None, 14, 14, 192) 576 conv2d_316[0][0]
__________________________________________________________________________________________________
batch_normalization_288 (BatchN (None, 14, 14, 256) 768 conv2d_319[0][0]
__________________________________________________________________________________________________
activation_285 (Activation) (None, 14, 14, 192) 0 batch_normalization_285[0][0]
__________________________________________________________________________________________________
activation_288 (Activation) (None, 14, 14, 256) 0 batch_normalization_288[0][0]
__________________________________________________________________________________________________
block8_7_mixed (Concatenate) (None, 14, 14, 448) 0 activation_285[0][0]
activation_288[0][0]
__________________________________________________________________________________________________
block8_7_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_7_mixed[0][0]
__________________________________________________________________________________________________
block8_7 (Lambda) (None, 14, 14, 2080) 0 block8_6_ac[0][0]
block8_7_conv[0][0]
__________________________________________________________________________________________________
block8_7_ac (Activation) (None, 14, 14, 2080) 0 block8_7[0][0]
__________________________________________________________________________________________________
conv2d_321 (Conv2D) (None, 14, 14, 192) 399360 block8_7_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_290 (BatchN (None, 14, 14, 192) 576 conv2d_321[0][0]
__________________________________________________________________________________________________
activation_290 (Activation) (None, 14, 14, 192) 0 batch_normalization_290[0][0]
__________________________________________________________________________________________________
conv2d_322 (Conv2D) (None, 14, 14, 224) 129024 activation_290[0][0]
__________________________________________________________________________________________________
batch_normalization_291 (BatchN (None, 14, 14, 224) 672 conv2d_322[0][0]
__________________________________________________________________________________________________
activation_291 (Activation) (None, 14, 14, 224) 0 batch_normalization_291[0][0]
__________________________________________________________________________________________________
conv2d_320 (Conv2D) (None, 14, 14, 192) 399360 block8_7_ac[0][0]
__________________________________________________________________________________________________
conv2d_323 (Conv2D) (None, 14, 14, 256) 172032 activation_291[0][0]
__________________________________________________________________________________________________
batch_normalization_289 (BatchN (None, 14, 14, 192) 576 conv2d_320[0][0]
__________________________________________________________________________________________________
batch_normalization_292 (BatchN (None, 14, 14, 256) 768 conv2d_323[0][0]
__________________________________________________________________________________________________
activation_289 (Activation) (None, 14, 14, 192) 0 batch_normalization_289[0][0]
__________________________________________________________________________________________________
activation_292 (Activation) (None, 14, 14, 256) 0 batch_normalization_292[0][0]
__________________________________________________________________________________________________
block8_8_mixed (Concatenate) (None, 14, 14, 448) 0 activation_289[0][0]
activation_292[0][0]
__________________________________________________________________________________________________
block8_8_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_8_mixed[0][0]
__________________________________________________________________________________________________
block8_8 (Lambda) (None, 14, 14, 2080) 0 block8_7_ac[0][0]
block8_8_conv[0][0]
__________________________________________________________________________________________________
block8_8_ac (Activation) (None, 14, 14, 2080) 0 block8_8[0][0]
__________________________________________________________________________________________________
conv2d_325 (Conv2D) (None, 14, 14, 192) 399360 block8_8_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_294 (BatchN (None, 14, 14, 192) 576 conv2d_325[0][0]
__________________________________________________________________________________________________
activation_294 (Activation) (None, 14, 14, 192) 0 batch_normalization_294[0][0]
__________________________________________________________________________________________________
conv2d_326 (Conv2D) (None, 14, 14, 224) 129024 activation_294[0][0]
__________________________________________________________________________________________________
batch_normalization_295 (BatchN (None, 14, 14, 224) 672 conv2d_326[0][0]
__________________________________________________________________________________________________
activation_295 (Activation) (None, 14, 14, 224) 0 batch_normalization_295[0][0]
__________________________________________________________________________________________________
conv2d_324 (Conv2D) (None, 14, 14, 192) 399360 block8_8_ac[0][0]
__________________________________________________________________________________________________
conv2d_327 (Conv2D) (None, 14, 14, 256) 172032 activation_295[0][0]
__________________________________________________________________________________________________
batch_normalization_293 (BatchN (None, 14, 14, 192) 576 conv2d_324[0][0]
__________________________________________________________________________________________________
batch_normalization_296 (BatchN (None, 14, 14, 256) 768 conv2d_327[0][0]
__________________________________________________________________________________________________
activation_293 (Activation) (None, 14, 14, 192) 0 batch_normalization_293[0][0]
__________________________________________________________________________________________________
activation_296 (Activation) (None, 14, 14, 256) 0 batch_normalization_296[0][0]
__________________________________________________________________________________________________
block8_9_mixed (Concatenate) (None, 14, 14, 448) 0 activation_293[0][0]
activation_296[0][0]
__________________________________________________________________________________________________
block8_9_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_9_mixed[0][0]
__________________________________________________________________________________________________
block8_9 (Lambda) (None, 14, 14, 2080) 0 block8_8_ac[0][0]
block8_9_conv[0][0]
__________________________________________________________________________________________________
block8_9_ac (Activation) (None, 14, 14, 2080) 0 block8_9[0][0]
__________________________________________________________________________________________________
conv2d_329 (Conv2D) (None, 14, 14, 192) 399360 block8_9_ac[0][0]
__________________________________________________________________________________________________
batch_normalization_298 (BatchN (None, 14, 14, 192) 576 conv2d_329[0][0]
__________________________________________________________________________________________________
activation_298 (Activation) (None, 14, 14, 192) 0 batch_normalization_298[0][0]
__________________________________________________________________________________________________
conv2d_330 (Conv2D) (None, 14, 14, 224) 129024 activation_298[0][0]
__________________________________________________________________________________________________
batch_normalization_299 (BatchN (None, 14, 14, 224) 672 conv2d_330[0][0]
__________________________________________________________________________________________________
activation_299 (Activation) (None, 14, 14, 224) 0 batch_normalization_299[0][0]
__________________________________________________________________________________________________
conv2d_328 (Conv2D) (None, 14, 14, 192) 399360 block8_9_ac[0][0]
__________________________________________________________________________________________________
conv2d_331 (Conv2D) (None, 14, 14, 256) 172032 activation_299[0][0]
__________________________________________________________________________________________________
batch_normalization_297 (BatchN (None, 14, 14, 192) 576 conv2d_328[0][0]
__________________________________________________________________________________________________
batch_normalization_300 (BatchN (None, 14, 14, 256) 768 conv2d_331[0][0]
__________________________________________________________________________________________________
activation_297 (Activation) (None, 14, 14, 192) 0 batch_normalization_297[0][0]
__________________________________________________________________________________________________
activation_300 (Activation) (None, 14, 14, 256) 0 batch_normalization_300[0][0]
__________________________________________________________________________________________________
block8_10_mixed (Concatenate) (None, 14, 14, 448) 0 activation_297[0][0]
activation_300[0][0]
__________________________________________________________________________________________________
block8_10_conv (Conv2D) (None, 14, 14, 2080) 933920 block8_10_mixed[0][0]
__________________________________________________________________________________________________
block8_10 (Lambda) (None, 14, 14, 2080) 0 block8_9_ac[0][0]
block8_10_conv[0][0]
__________________________________________________________________________________________________
conv_7b (Conv2D) (None, 14, 14, 1536) 3194880 block8_10[0][0]
__________________________________________________________________________________________________
conv_7b_bn (BatchNormalization) (None, 14, 14, 1536) 4608 conv_7b[0][0]
__________________________________________________________________________________________________
conv_7b_ac (Activation) (None, 14, 14, 1536) 0 conv_7b_bn[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_3 (Glo (None, 1536) 0 conv_7b_ac[0][0]
__________________________________________________________________________________________________
dense_12 (Dense) (None, 516) 793092 global_average_pooling2d_3[0][0]
__________________________________________________________________________________________________
dropout_6 (Dropout) (None, 516) 0 dense_12[0][0]
__________________________________________________________________________________________________
dense_13 (Dense) (None, 256) 132352 dropout_6[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (None, 256) 0 dense_13[0][0]
__________________________________________________________________________________________________
dense_14 (Dense) (None, 64) 16448 dropout_7[0][0]
__________________________________________________________________________________________________
dense_15 (Dense) (None, 2) 130 dense_14[0][0]
==================================================================================================
Total params: 55,278,758
Trainable params: 13,383,078
Non-trainable params: 41,895,680
__________________________________________________________________________________________________
None
In [24]:
# Define modifier to replace the sigmoid function of the last layer to a linear function
def model_modifier(m):
m.layers[-1].activation = tf.keras.activations.linear
# Define losses functions. 0 is the index for a normal MRI
loss_normal = lambda output: K.mean(output[:, 0])
# Define losses functions. 1 is the index for a diffuse malformation of cortical development MRI
loss_diffuseMCD = lambda output: K.mean(output[:, 1])
# Create Gradcam object
gradcam = Gradcam(model, model_modifier)
# Create Saliency object
saliency = Saliency(model, model_modifier)
# Iterate through the MRIs in test set
# Set background to white color
plt.rcParams['axes.facecolor']='white'
plt.rcParams['figure.facecolor']='white'
plt.rcParams['figure.edgecolor']='white'
print('\n \n' + '\033[1m' + 'EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW)' + '\033[0m' + '\n')
print('\033[1m' + 'EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION) \n \nHIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI' + '\033[0m'+ '\n')
# print images 100 to 199
for i in range(100, 199):
# Print spaces to separate from the next image
print('\n \n \n \n \n \n')
# Print real classification of the image
if y_true[i]==0:
real_classification='Normal MRI'
elif y_true[i]==1:
real_classification='Diffuse MCD'
print('\033[1m' + 'REAL CLASSIFICATION OF THE IMAGE: {}'.format(real_classification) + '\033[0m')
# Print model classification and model probability of MCD
if y_predInceptionResNetV2[i]==0:
predicted_classification='Normal MRI'
elif y_predInceptionResNetV2[i]==1:
predicted_classification='Diffuse MCD'
print('\033[1m' + 'MODEL CLASSIFICATION OF THE IMAGE: {}'.format(predicted_classification) + '\033[0m \n')
print('\033[1m' + ' Prob. Normal MRI: {:.4f} '.format(testInceptionResNetV2[i][0]) + 'Prob. Diffuse MCD: {:.4f} '.format(testInceptionResNetV2[i][1]) + '\033[0m')
# Arrays to plot
original_image=shuffled_test_X[i]
list_heatmaps=[
# GradCam heatmap for normal MRI
normalize(gradcam(loss_normal, shuffled_test_X[i])),
# GradCam heatmap for diffuse MCD
normalize(gradcam(loss_diffuseMCD, shuffled_test_X[i])),
# Saliency heatmap for normal MRI
normalize(saliency(loss_normal, seed_input=np.expand_dims(shuffled_test_X[i], axis=0), smooth_noise=0.2)),
# Saliency heatmap for diffuse MCD
normalize(saliency(loss_diffuseMCD, seed_input=np.expand_dims(shuffled_test_X[i], axis=0), smooth_noise=0.2))
]
# Define figure
f=plt.figure(figsize=(20, 8))
# Define the image grid
grid = ImageGrid(f, 111,
nrows_ncols=(2, 2),
axes_pad=0.05,
share_all=True,
cbar_location="right",
cbar_mode=None,
cbar_size="2%",
cbar_pad=0.15)
# Iterate over the graphs
for j, axis in enumerate(grid):
# Plot original
im=axis.imshow(original_image)
im=axis.imshow(list_heatmaps[j][0], cmap='jet', alpha=0.5*testInceptionResNetV2[i][j%2])
im=axis.set_xticks([])
im=axis.set_yticks([])
# Create scalarmappable for obtaining the colorbar from 0 to 1
sm = plt.cm.ScalarMappable(cmap='jet', norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
plt.show()
EACH ORIGINAL MRI IS ANALYZED WITH TWO METHODS: CLASS ACTIVATION MAP (UPPER ROW) AND SALIENCY MAP (LOWER ROW) EACH MAP IS SUPERIMPOSED ON THE ORIGINAL MRI WITH A TRANSPARENCY THAT IS INVERSELY PROPORTIONAL TO THE ESTIMATED PROBABILITY OF THE MRI BELONGING TO THAT CATEGORY (NORMAL MRI, DIFFUSE CORTICAL MALFORMATION) HIGHER ESTIMATED PROBABILITIES PRODUCE CLEARLY SEEN MAPS OVERLAID ON THE ORIGINAL MRI AND LOWER ESTIMATED PROBABILITIES PRODUCE VERY TRANSPARENT OR NOT APPRECIABLE MAPS OVERLAID ON THE ORIGINAL MRI REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9994 Prob. Diffuse MCD: 0.0006
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0050 Prob. Diffuse MCD: 0.9950
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9894 Prob. Diffuse MCD: 0.0106
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9805 Prob. Diffuse MCD: 0.0195
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9926 Prob. Diffuse MCD: 0.0074
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0006 Prob. Diffuse MCD: 0.9994
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.6663 Prob. Diffuse MCD: 0.3337
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.1366 Prob. Diffuse MCD: 0.8634
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9536 Prob. Diffuse MCD: 0.0464
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9999 Prob. Diffuse MCD: 0.0001
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9999 Prob. Diffuse MCD: 0.0001
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.3543 Prob. Diffuse MCD: 0.6457
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.7574 Prob. Diffuse MCD: 0.2426
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0001 Prob. Diffuse MCD: 0.9999
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0670 Prob. Diffuse MCD: 0.9330
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.1737 Prob. Diffuse MCD: 0.8263
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0038 Prob. Diffuse MCD: 0.9962
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9999 Prob. Diffuse MCD: 0.0001
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9951 Prob. Diffuse MCD: 0.0049
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.4653 Prob. Diffuse MCD: 0.5347
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.6236 Prob. Diffuse MCD: 0.3764
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0004 Prob. Diffuse MCD: 0.9996
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.9898 Prob. Diffuse MCD: 0.0102
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 0.6985 Prob. Diffuse MCD: 0.3015
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.1012 Prob. Diffuse MCD: 0.8988
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
REAL CLASSIFICATION OF THE IMAGE: Diffuse MCD MODEL CLASSIFICATION OF THE IMAGE: Diffuse MCD Prob. Normal MRI: 0.0000 Prob. Diffuse MCD: 1.0000
REAL CLASSIFICATION OF THE IMAGE: Normal MRI MODEL CLASSIFICATION OF THE IMAGE: Normal MRI Prob. Normal MRI: 1.0000 Prob. Diffuse MCD: 0.0000
